PanMETAI - a high performance tabular foundation model for accurate pancreatic cancer diagnosis via NMR metabolomics

Domain: neuroscience
Published: 2026-02-13
Editorial outcome: minor_revision

Abstract
Late diagnosis and the lack of effective early detection techniques contribute to the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). To address this challenge, we develop 1H NMR-based metabolomics–AI platforms employing customized multilayer support vector machine (SVM), AutoGluon, and Tabular Foundation Model (TabPFN) frameworks. These platforms integrate serum metabolomic profiles—including small-molecule metabolites and lipoproteins—with clinical/biochemical parameters (age, CA19-9) and Activin A, derived from 902 participants (424 high-risk controls and 478 PDAC cases). Our TabPFN-based algorithm, PanMETAI, outperform state-of-the-art models. In the Taiwanese training and validation cohort, the model achieved an impressive AUC of 0.99 (95% CI: 0.98–0.99). Its robustness is further confirmed in a Lithuanian external validation cohort (n = 322), which yields an AUC of 0.93 (0.90-0.95). Notably, it identifies key signature patterns that improve early-stage (I/II) PDAC diagnosis and perform well with small sample sizes (n = 50). TabPFN-PanMETAI offers a rapid, accurate, and non-invasive tool for early PDAC detection, with strong potential for clinical application.

Introduction
Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy with a dismal 13% 5-year survival rate1, primarily due to late-stage diagnosis2, underscoring a critical unmet need for reliable early detection as current diagnostic approaches lack sufficient specificity and sensitivity3–5. The serological biomarker, carbohydrate antigen 19-9 (CA19-9)6,7, exhibits limited utility due to its elevated levels in various nonmalignant conditions8,9, rendering it insufficient as a standalone diagnostic marker. Despite extensive studies have explored alternative PDAC biomarkers—including liquid biopsy approaches using protein and gene markers10, cell-free DNA11, microRNAs12, and exosomes13—limitations in large-scale validation and clinical superiority over CA19-9 persist. As a result, current biomarkers remain inadequate for accurate, rapid, and cost-effective PDAC diagnosis in clinical settings.

Metabolomics, the study of metabolites and lipids within biological systems, offers a promising strategy for discovering PDAC biomarkers14,15. Disrupted metabolic homeostasis is increasingly recognized as a key contributor to PDAC tumorigenesis16,17. Several studies have identified metabolic and lipidomic biomarkers with potential diagnostic value18–21. However, reliance on a limited set of biomarkers is insufficient to capture the full complexity of PDAC and often suffers from issues of validation or reproducibility. This limits their effectiveness in distinguishing early-stage patients, particularly in high-risk populations, underscoring the need for a more comprehensive and robust diagnostic approach.

This study introduces a powerful approach to improve early detection of PDAC using high-resolution proton nuclear magnetic resonance (1H NMR) spectroscopy. We generated comprehensive, label-free serum metabolomic fingerprints and integrated these with clinical/biochemical parameters and a prognostic protein biomarker to develop a multilayer diagnostic framework. This approach overcomes the limitations of conventional biomarker-driven methods by enabling practical interpretation of complex NMR spectra and integrating heterogeneous data to enhance early-stage cancer detection accuracy.

We leveraged recent advancements in artificial intelligence (AI) to evaluate the diagnostic performance of several state-of-the-art models, including the transformer-based neural network (Tabular Foundation Model) TabPFN22 and the AutoGluon AutoML platforms23. Through systematic optimization, we established a fast, effective, and highly accurate algorithm, PanMETAI (a TabPFN-based algorithm integrating NMR metabolomics, age, CA19-9, and Activin A) for PDAC detection.

Results
Developing machine learning approaches for enhanced pancreatic cancer detection using metabolic features, Age, CA19-9, and Activin A

An overview of the study design is presented in Fig. 1. Serum samples from 350 participants in the exploration cohort (Table 1) and 552 participants in the validation cohorts (Table 2) were divided into three datasets for machine learning (ML) development and validation (Fig. 1A). To simulate a clinically challenging diagnostic setting, we adopted a structured data-splitting strategy to train a generalized ML model. The first cohort was divided into a training dataset [n = 200; 100 high-risk controls (HRC) and 100 patients with stage I/II PDAC] and a developing (validation) test dataset (n = 150; 50 HRC and 100 patients with stage III/IV PDAC). The second cohort was used as an independent, blinded validation dataset (n = 552), designed to evaluate the model’s performance in real-world clinical scenarios. Each dataset included both HRC (non-cancer) and PDAC (cancer) samples. Samples with missing values in clinical/biochemical parameters were excluded from ML prediction analyses.

For machine learning input, we collected three data modalities: NMR-based metabolomic spectra, protein biomarker levels, and clinical/biochemical parameters. Metabolomic profiling using 600 MHz NMR with a high-throughput SampleJet system generated both 1H NMR-M (small molecule metabolites) and NMR-M+L (metabolites plus lipoproteins) spectra. These raw spectra were then processed through a standardized pipeline involving internal standard (TSP-d4) normalization, signal trimming, and water signal removal, ultimately yielding 71,074 (Supplemental Fig. 1) and 42,500 (Supplemental Fig. 2) features, respectively. Given Activin A’s reported association with early-stage PDAC and poor prognosis24,25, suggesting it plays a critical role in pancreatic cancer development. Therefore, serum Activin A level was included as a protein feature (Supplemental Fig. 3). In addition, recognizing eight clinical/biochemical parameters (age, gender, smoking, Body Mass Index (BMI), CA19-9, Carcinoembryonic antigen (CEA), Hemoglobin A1c (HbA1c), and fasting glucose (GluAC)) monitored by physicians may correlate with PDAC progression in high-risk individuals, so we evaluated these factors. This analysis, using Silhouette scores on UMAP clustering, identified age and CA19-9 as the variables offering the best discrimination between cancer and non-cancer cases (score: 0.35, Supplementary Fig. 4). Therefore, our final integrated dataset for model development comprised NMR-M, NMR-M+L, Activin A, age, and CA19-9 (Fig. 1A).

To handle the high dimensionality and heterogeneity of our input data, particularly the extensive spectral features (~40K-70K), we developed a multilayer support vector machine (SVM) framework (Fig. 1B, Materials and Methods). This architecture employed tailored feature selection strategies based on spectral type: SelectKBest for NMR-M and recursive feature elimination (RFE) for NMR-M+L (Supplemental Fig. 5). The resulting selected features were transformed into decision values by individual SVMs and subsequently integrated with clinical/biochemical parameters and protein features. This design aimed to prevent any single data modality from disproportionately influencing the model while retaining comprehensive spectral information, thus ensuring a balanced contribution to the prediction. The multilayer structure also enabled independent optimization and hyperparameter tuning of each SVM component before fusion, enhancing model development and flexibility.

To benchmark the performance of our multilayer SVM framework, we compared it against several established machine learning algorithms, including k-nearest neighbors (kNN), gradient boosting (GB), decision trees, random forests, and neural networks. Most of these were implemented using the AutoGluon platform, which provides robust and user-friendly ML workflows. AutoGluon’s ensemble model (14 models) utilized the same feature selection approach as our multilayer architecture (Supplemental Fig. 5). Finally, we also evaluated TabPFN, a transformer-based method incorporating attention layers and in-context learning. Due to TabPFN’s input size limitation (<500 features), we applied a modified feature selection strategy (Supplemental Fig. 6), selecting 215 features from NMR-M and 255 from NMR-M+L for integration with the clinical/biochemical parameters and protein data (Fig. 1B).

TabPFN demonstrates superior performance in PDAC detection

We evaluated the performance of various machine learning algorithms on the Developmental Test (Dev-Test) dataset. Our multilayer SVM demonstrated robust predictive capabilities, achieving an AUC of 0.96 (95% CI: 0.93–0.99), sensitivity of 0.96 (0.92–0.99), specificity of 0.83 (0.71–0.94), an overall accuracy of 0.92 (0.88–0.96), and a J-Statistic of 0.79 (0.67–0.90). Among the AutoGluon 1.1.0 models, XGBoost exhibited the highest performance, with an AUC of 0.97 (0.93–0.99) and a J-Statistic of 0.83 (0.73–0.93). TabPFN outperformed all other tested methods across nearly all metrics (Supplemental Table 1). It demonstrated high performance, achieving an AUC of 0.98 (0.94–1.00), sensitivity of 0.98 (0.95–1.00), specificity of 0.90 (0.81–0.98), accuracy of 0.96 (0.92–0.99), and a J-Statistic of 0.88 (0.78–0.96) when integrating all data modalities, while notably requiring less training time and no hyperparameter tuning.

To validate the top three Dev-Test algorithms (multilayer SVM, XGBoost, and TabPFN), we conducted a blind test and confusion matrix analysis (Fig. 2 and Table 3), which confirmed TabPFN’s superior performance. In this test, TabPFN achieved an high AUC of 0.99 (0.98–0.99), accuracy of 0.94 (0.91–0.95), sensitivity of 0.93 (0.89–0.96), and specificity of 0.94 (0.91–0.97). In contrast, the multilayer SVM and XGBoost methods were less effective, reaching overall accuracies of approximately 0.90 and 0.93, respectively. The final algorithm, named TabPFN-PanMETAI (integrating NMR metabolomics, age, CA19-9, and Activin A), demonstrated significant robustness and generalizability in the external validation test on a Lithuanian cohort (Table 2), yielding an AUC of 0.93 (0.90–0.95), sensitivity 0.90 (0.85–0.94), and specificity 0.83 (0.77–0.89), with consistent performance even for early-stage (Stage I/II) patients (Table 4). The consistent success across two geographically and behaviorally distinct populations, Taiwan and Lithuania, provides compelling evidence of TabPFN-PanMETAI’s strong potential for widespread clinical implementation.

1H NMR metabolomics in TabPFN-PanMETAI enhances early-stage PDAC detection

TabPFN-PanMETAI demonstrated high performance in the Taiwanese cohort across different PDAC stages. For detecting late-stage PDAC (III/IV), the model shown high effectiveness, achieving an accuracy of 0.95 (0.93–0.97), a sensitivity of 0.95 (0.92–0.97), a specificity of 0.95 (0.92–0.97), and an AUC of 0.99 (0.98–0.99). Importantly, TabPFN-PanMETAI also maintained high diagnostic efficacy for early-stage PDAC (I/II), achieving an accuracy of 0.92 (0.89–0.95), sensitivity of 0.78 (0.64–0.91), specificity of 0.95 (0.92–0.97), and an AUC of 0.95 (0.90–0.98) (Supplemental Table 2). Comparable results were also observed in the Lithuania cohort (Table 4). To evaluate which data modalities drove the model’s superior performance, we analyzed early and late PDAC detection using NMR metabolomics alone, clinical/biochemical parameters and protein data alone, and the fully integrated model. While our findings revealed that clinical/biochemical parameters and protein data alone were sufficient for achieving high performance in detecting late-stage (III/IV) PDAC, the integration of all data types led to a significant increase in detection sensitivity specifically for early-stage PDAC (Fig. 3 and Supplemental Table 2). This crucial result strongly supports the essential role of capturing subtle metabolic alterations within the NMR spectra, confirming that the inclusion of NMR data in TabPFN-PanMETAI is fundamental to enhancing early diagnosis.

Metabolomic Feature Selection and SHAP Analysis uncover TabPFN-PanMETAI’s Biological Insights

Given the crucial role of NMR metabolomics within TabPFN-PanMETAI for effective cancer classification, we investigated the specific features prioritized by this algorithm to gain insights into its decision-making process and enhance interpretability. To understand why NMR-based metabolomic data strengthens the model’s early predictive capabilities, we mapped the 255 and 215 metabolic features selected by TabPFN onto the 1H NMR-M+L and NMR-M spectra, respectively (Fig. 4A). The corresponding metabolite identifications are detailed in the Fig. 4 legend, revealing a total of 11 metabolites and 2 lipids from NMR-M+L, and 6 metabolites from NMR-M.

Subsequent quantification of these identified metabolites using the Bruker IVDr platform, cross-referencing with the IVDr database, revealed that 10 were quantifiable and exhibited statistically significant differences between cancer and high-risk control (HRC) groups (Fig. 4B). Notably, these metabolic alterations proved stage-dependent and align with prior research26–31. In early-stage (Stages I/II) PDAC, distinct changes were observed, including significant decreases in HDL and glutamine levels and marked elevation of lactic acid, TMAO, ornithine, glutamic acid, and glucose. In contrast, the metabolic profile became dynamic in advanced stages: some metabolites (like TMAO and Threonine) showed a trend toward normalization, while others (such as glutamic acid and glucose) remained persistently elevated. This finding highlights the dynamic role of metabolism throughout PDAC progression, emphasizing its crucial involvement in the early stages of tumor development, a pattern further confirmed by similar metabolite changes observed in the Lithuania cohort (Supplementary Fig. 7).

To further elucidate the TabPFN-PanMETAI model’s decision-making process, we performed SHAP (SHapley Additive exPlanations) analysis32 to assess the contribution of individual features. As depicted in Fig. 4C and Supplemental Table 3, the SHAP analysis highlighted those features at NMR-M+L ppm 1.23/3.27/3.74 (corresponding to HDL, VLDL, Glucose, TMAO, and Alanine) and NMR-M ppm 2.93/2.96 (corresponding to L-Asparagine, 3-Aminoisobutanoic acid, 2-Oxoglutaric acid, and L-Tyrosine) exerted a substantial impact on the model’s ability to distinguish PDAC from high-risk populations. These findings underscore the interpretability of the TabPFN-PanMETAI model and the biological relevance of its feature selection, bolstering its translational potential in precision medicine.

To gain deeper biological insights, we conducted metabolic pathway and interaction network analyses using MetaboAnalyst 6.0 and Ingenuity Pathway Analysis (IPA), as illustrated in Fig. 5. The metabolic pathway enrichment map (Fig. 5A) confirmed that the SHAP-annotated metabolites were significantly enriched in key dysregulated pathways in PDAC, including the Glucose-Alanine Cycle, Phenylalanine and Tyrosine Metabolism, Aspartate Metabolism, and the Malate-Aspartate Shuttle, thus reinforcing the relevance of global alterations in amino acids and energy pathways33,34. The metabolic network (Fig. 5B) further highlighted molecules such as L-glutamate, alanine, pyruvate, cholesterol, and triglycerides as central hubs of dysregulation within mitochondrial and lipid metabolism. Finally, Fig. 5C summarized the disease and biofunctions derived from the SHAP features, with top enriched categories including cancer, organismal injury, and lipid metabolism, consistent with hallmark biological themes in PDAC progression. Collectively, these multi-layered analyses support the mechanistic relevance of TabPFN-PanMETAI’s feature selection and provide crucial system-level insights into PDAC metabolic dysregulation.

Robust TabPFN performance with small datasets

Developing robust diagnostic models for low-incidence cancers like PDAC often faces the challenge of limited patient cohorts at individual centers. Traditional machine learning methods typically require large datasets for effective generalization, a constraint difficult to overcome in single-center studies. In contrast, TabPFN22 has demonstrated competitive performance on small tabular datasets without hyperparameter tuning, utilizing in-context learning from synthetic priors to approximate Bayesian inference. To assess the impact of training set size on our TabPFN-PanMETAI model’s early PDAC detection capabilities, we conducted a subsampling analysis. We randomly selected varying numbers of stage I/II PDAC patients (n = 3–199) for training and evaluated the model on an independent blind test set over 20 repetitions. Our analysis revealed a rapid improvement in model performance (AUC, sensitivity, specificity, accuracy) with increasing training sample size. Notably, TabPFN-PanMETAI achieved stable high accuracy (≈0.90) with as few as 50–70 training cases (Fig. 6, Supplemental Fig. 8). The consistent and robust performance observed with such a limited training dataset (n = 50) is remarkable. These results underscore the potential of TabPFN’s in-context learning paradigm for enabling robust diagnostic modeling in small clinical cohorts, offering a solution for medical applications where large datasets are not feasible.

Methods
Study population
Our study analyzed a total of 1224 serum samples collected from two geographically distinct institutions: 902 from the National Taiwan University Hospital (NTUH) (January 2004–December 2022) and 322 from the Lithuanian University of Health Sciences (LSMU) (October 2015–June 2024). The study cohort was systematically partitioned to support the development and validation of machine learning. A total of 478 confirmed PDAC patients and 424 high-risk individuals (HRIs) with a family history of PDAC from Taiwan comprised the training and validation cohort, whereas 172 confirmed PDAC patients and 150 control subjects from Lithuania constituted the external validation cohort. All patients provided written informed consent prior to treatment, and diagnoses were pathologically confirmed. Samples were obtained under standardized fasting conditions and isolated from peripheral blood. This comprehensive cohort structure complies with all relevant ethical regulations, approved by both the NTUH Institutional Review Board (201301048RIND) and the Kaunas Regional Biomedical Research Ethics Committee (BE-2-31), was essential for developing a robust predictive model and validating its generalizability across diverse clinical settings (Participant demographics are detailed in Table 1 and Table 2).

Serum sample preparation
All serum samples were prepared from blood collected under fasting conditions using a serum-separating tube (BD Vacutainer Systems, Franklin Lakes, NJ, USA) following a standard venous blood sampling protocol. The samples were centrifuged at 3000g for 10 min at 4°C, then stored at −80°C until use. Before 1H NMR spectroscopic analysis, frozen serum samples were thawed on ice for 30 min and centrifuged for 10 min at 13000g at 4°C. Serum samples were prepared using 3 mm Ø, 5-long SampleJet NMR tubes (Brucker BioSpin GmbH, Rheinstetten, Germany). For standard sample preparation, 110 μL of serum was mixed with 110 μL phosphate buffer (75 mM Na2HPO4, 2 mM NaN3, 4·6 mM TSP in D2O), centrifuged for 10 s, and 200 μL was transferred to a 3 mm NMR tube for analyses.

1H NMR acquisition
The 1H NMR spectra were acquired using a 600 MHz Bruker Avance III HD spectrometer equipped with a 5 mm BBI probe and fitted with the Bruker SampleJet robot cooling system set to 4°C. A standard one-dimensional (1D) pulse sequence with solvent presaturation experiment included both small molecule metabolites and lipoproteins (32 scans, zero-filled to 128 K refers to NMR-M+L spectrum), whereas a Carr−Purcell−Meiboom−Gill spin−echo experiment was used to identify small molecule metabolites (32 scans, zero-filled to 128 K refers to NMR-M spectrum). The data were processed in automation using a Bruker Topspin 3·6·2 and ICON NMR to achieve phasing and baseline correction. The TSP-d4 provided a chemical shift reference (1H, δ 0·00). All spectra passed the Bruker in vitro diagnostics research (IVDr) validation criteria51—covering water suppression quality, line width (≤1.50 Hz for reference metabolite, Alanine), and shim—were included for downstream machine learning analysis.

The quantification of serum metabolites and lipoproteins were completed using the Bruker IVDr methods. A total of 39 metabolites and 112 lipoprotein parameters were generated using the IVDr Quantification in Plasma/Serum (B.I.Quant-PS) and Lipoprotein Subclass Analysis (B.I.LISA) methods52.

Data processing of 1H NMR spectra for ML
Two types of spectra (NMR-M & -M+L) were measured, one is metabolite-specific (NMR-M) and another, lipoprotein-containing (NMR-M+L). The NMR spectra were read using the nmrglue module53. A Fourier-transformed NMR spectrum contains a list of signal intensities corresponding to the 1H chemical shifts. After acquiring the reads, each spectrum was normalized by its internal standard TSP-d4 for handling bench differences. The normalized spectrum was further trimmed for removing regions that do not contain meaningful information: for NMR-M, the data with frequencies lower than or equal to 0.13 ppm, or higher than or equal to 11.13 ppm were removed; for NMR-M+L, lower than or equal to 0.11 ppm or higher than or equal to 10.00 ppm were trimmed; for both NMR spectra, data between 4.64 ppm and 4.78 ppm was removed due to the water signal. After normalization and trimming, the resulting spectra were used as inputs for the following ML pipeline.

ML Feature Selection
Each signal intensity in the spectrum is considered a separate feature. As the trimmed NMR spectrum contains 71,074 (NMR-M) and 42,500 (NMR-M+L) features, we further reduce their number using feature selection in order to extract the most relevant information.

We rank features using different methods: NMR-M with f_classif, an F-test-based algorithm, and NMR-M+L with a linear SVM, both from the scikit-learn package (version 1·2·2). Feature selection is performed using SelectKBest for NMR-M and recursive feature elimination (RFE) for NMR-M+L, both from scikit-learn. The different methods chosen for NMR-M and NMR-M+L are motivated by differences in their spectral profiles, as NMR-M+L generally contains broader signals. In order to access the discriminating power, selected features are clustered using UMAP54 [umap package (version 0.5.7)], and the Silhouette score [cuml package (version 24.10.00)] for clusters of healthy and diseased samples is calculated. With an iteration through different numbers of features picked, we searched for the features that yield the highest Silhouette score, and the corresponding features are used. With this process, we selected 1,563 features of NMR-M and 6,800 features of NMR-M+L for the multilayer SVM and AutoGluon models. For TabPFN, due to the constraints on feature numbers, we select 215 features of NMR-M and 255 features of NMR-M+L.

Missing data handling
All patients with missing or NA (Not Applicable) values in clinical/biochemical records are excluded from the dataset to provide a fair basis for benchmarking (Table S1) for all ML models. The precise numbers for each model are presented in ROC-AUC figures.

Model evaluation pipeline
The training dataset was used to train the model, and the Dev-Test was used for multilayer hyperparameters (HPs) tuning. Four indices were used to benchmark each model’s performance: accuracy, specificity, sensitivity, and area under the receiver operating characteristic (ROC) curve (AUC). After HPs tuning upon Dev-Test, the tuned models (multilayer SVM), XGBoost from AutoGluon, and TabPFN were compared, and the champion model was selected for the final performance test on the blind test dataset.

Model architectures
AutoGluon provides detailed descriptions for each of its implemented models: https://auto.gluon.ai/stable/api/autogluon.tabular.models.html23.

The description of TabPFN can be found in the original paper22.

The customized architecture for multilayer SVM is arranged to handle the multimodal nature of our inputs. This architecture is composed of 3 layers: encoder, fusion, and final decision layers.

Encoder. For multimodal learning, an “encoder” was used to transform different data modalities into a representation to fuse later. We first scaled the input data with StandardScaler function [scikit-learn package (version 1.2.2)], followed by two independent SVMs derived from the two NMR spectra with their values of radial basis function (RBF) kernel as the “decision value.” We extracted such a value from the SVM as the new representation of the original spectrum, and it was subsequently fused with clinical/biochemical parameters and protein data.

Fusion. In general, there are several methods for fusing modalities in multimodal learning. Here, we simply chose to concatenate the data. Following the encoder layer, the decision values were concatenated with clinical/biochemical parameters and protein data.

Final decision. The concatenated features were scaled again using StandardScaler, followed by another SVM for the final decisions.

Measurement of Activin A levels in serum
Serum levels of Activin A were quantified using commercially available sandwich enzyme-linked immunosorbent assay kits (DAC00B, R&D Systems, Inc.) based on the manufacturer’s instructions. Assays were performed duplicate for both biomarkers. Activin A levels were measured after a twofold dilution of the serum samples, followed by absorbance measurement at 450 nm with the same spectrophotometer. Activin A data is presented as pg/mg, which was normalized to the total protein (mg/mL) in the serum sample.

Statistical analysis
The biochemical results are presented as the means and standard deviations. They were analyzed according to the statistical methods available in GraphPad Prism 10·0 (San Diego, CA). Most metrics in ML models are neither normally distributed (confirmed by Kolmogorov–Smirnov test) nor having an equal variance (confirmed by Levene test), we performed Wilcoxon Signed Rank test to tackle this data distribution. All statistical tests were performed using the stats module from the SciPy library (version 1.14.1). Significant differences were presented with P-values < 0·05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary:\nThis paper presents PanMETAI, a tabular foundation model based on TabPFN, developed for the accurate and early\ndetection of pancreatic ductal adenocarcinoma (PDAC) using ¹H NMR metabolomics, clinical data (age, CA19-9), and the\nprotein biomarker Activin A. The study encompasses 902 participants and employs a three-tier data division (training,\ndevelopment, and blind test) to build and validate models, including multilayer SVM, AutoGluon-XGBoost, and TabPFN.\nPanMETAI demonstrates outstanding diagnostic performance (AUC up to 0.99, accuracy ~94%), particularly in early-stage\ndetection, and proves robust even with small datasets. The study includes feature interpretability via SHAP analysis and\nmetabolite quantification, positioning PanMETAI as a non-invasive, scalable diagnostic aid.\nPositives:\n1- The paper impressively integrates NMR spectral features with clinical and biomarker data, addressing a real-world\nchallenge in early cancer diagnostics.\n2- PanMETAI, leveraging TabPFN, exhibits high accuracy and robustness with small training sets, a notable advancement\nfor low-incidence disease modelling.\n3- The inclusion of SHAP analysis and biochemical quantification adds transparency and scientific validity, enhancing\ntranslational relevance.\nNegatives:\n- All data is derived from a single institution, limiting generalizability. Cross-center or international cohorts would strengthen\nthe conclusions.\n- While feature selection methods are described, the rationale for specific thresholds and their reproducibility across cohorts\nis not clearly justified.\nMajor points:\n- While TabPFN is presented as the foundation of the PanMETAI framework, the manuscript provides limited justification for\nits selection over other high-performing tabular models such as TabNet or FT-Transformer. Although the results are\nimpressive, a more critical discussion is warranted to contextualize TabPFN’s advantages—particularly its in-context\nlearning paradigm—in relation to PDAC’s clinical constraints. The model’s parameter-free nature and efficiency are\nacknowledged, but the novelty would be more compelling if benchmarked against a broader spectrum of foundation models,\nespecially those designed for biological datasets.\n- The study highlights metabolite-level changes and uses SHAP for feature attribution, but it stops short of performing deeper\nbiological pathway analysis or metabolic network interpretation. Mapping the identified features to disrupted biochemical\npathways (e.g., glutaminolysis, glycolysis, lipid remodelling) could significantly enhance the translational relevance of the\nfindings. Without this layer of biological insight, the model’s interpretability, while technically sound, remains disconnected\nfrom systems-level understanding of PDAC progression.\n- Several methodological gaps limit the robustness and generalizability of the model. First, no ablation study is performed to\nassess the individual contribution of each data modality or feature subset, which is crucial for clinical simplification. Second,\nalthough the data spans nearly two decades (2004–2022), the analysis does not investigate how diagnostic markers or\nmodel performance may vary over time, missing an opportunity to explore cohort drift or biomarker stability. Lastly, external\nvalidation using data from other institutions or populations is absent, significantly limiting clinical readiness claims.", "reviewer_2": "The authors use an MR spec serum signature of metabolites and lipoproteins along with CA19-9 and Activin A as a test for\npancreatic cancer and show that it achieves high performance in a validation set.\nOne limitation of the study is the age difference between the cases and controls, with a mean of ~15 years. The controls\nwere patients with familial/genetic risk. Although this difference is not likely to dramatically affect the results, certain\nmetabolic alterations increase with age, such as impaired glucose tolerance/DM and associated changes in lipids that could\nmodify the prediction. In addition, the number of stage I/II PDAC cases in the validation set is also quite small compared to\nthe number of Stage III/IV cases; 37 vs 241. Therefore, the diagnostic performance of the model is driven by advanced-stage\ncases, which is not an adequate test of model performance.", "reviewer_3": "This study presents PanMETAI, a transformer-based AI model (TabPFN) that integrates NMR metabolomics, clinical data\n(including age and CA19-9), and protein biomarkers (Activin A) for the diagnosis of pancreatic ductal adenocarcinoma\n(PDAC). Using 902 serum samples from high-risk controls and PDAC patients across different stages, the model achieved\noutstanding performance (AUC 0.99, accuracy 0.94) on a blinded validation cohort. Notably, PanMETAI improved early-\nstage detection and remained robust with small sample sizes, highlighting its potential for clinical deployment in low-\nincidence cancer diagnostics.\nThe choice of NMR as the primary metabolomics platform, while well justified in terms of throughput and quantitative\nreproducibility, may limit the detection of low-abundance yet biologically relevant metabolites due to its lower sensitivity and\nnarrower dynamic range compared to high-resolution mass spectrometry (HRMS). Although the authors briefly mention\nsome challenges associated with MS, it would strengthen the manuscript to provide a more balanced discussion. HRMS-\nbased metabolomics, particularly in targeted and semi-targeted formats, has evolved significantly, with robust\nstandardization protocols, high analytical sensitivity, and broad metabolite coverage. It remains a leading platform for the\ndiscovery of cancer biomarkers. The authors should clarify why HRMS was omitted and whether future iterations of\nPanMETAI might integrate MS-based data to further enhance the model’s diagnostic utility.\nMajor Methodological Concern: Sample Preparation for NMR Spectroscopy\nA critical limitation of this study lies in the serum sample preparation protocol used for ^1H NMR metabolomics. The\nmanuscript describes a direct dilution of native serum with phosphate buffer in D₂O (110 µL + 110 µL), but fails to address\nthe presence of intact serum proteins such as albumin and globulins. These macromolecules dominate the NMR spectrum\nand broaden signals of low-molecular-weight metabolites through T₂ relaxation and line broadening effects. Without protein\nremoval, either by organic solvent precipitation (e.g., methanol or acetonitrile) or ultrafiltration (e.g., with a 3 kDa or 10 kDa\ncutoff), the spectra are likely compromised, thereby undermining the accuracy and sensitivity of metabolite detection.\nAlthough the authors mention using CPMG sequences for metabolite detection, this is not a substitute for protein removal.\nCPMG suppresses broad macromolecular signals but does not eliminate their interference, especially in crowded regions of\nthe spectrum or when quantification is required.\nFurther, the use of a 1:1 dilution with phosphate buffer introduces significant concerns:\n• Dilution reduces signal intensity, particularly affecting low-abundance biomarkers.\n• Na₂HPO₄ can alter the chemical environment of pH-sensitive metabolites (e.g., citrate, lactate).\n• The preparation does not eliminate water, despite D₂O use. Without a detailed description of presaturation parameters,\nwater suppression quality, or the management of residual water signals, it is difficult to assess the reliability of the spectra.\nThis lack of standard protein-removal procedures represents a fundamental methodological flaw. Without addressing this\nissue, the validity of the downstream machine learning models, however robust, remains questionable, as they are ultimately\ntrained on spectra that may be distorted or biased by macromolecular contributions.\nRecommendation: The authors must either (1) repeat the NMR acquisition using appropriate protein-removal protocols or (2)\nprovide quantitative evidence (e.g., metabolite linewidths, S/N ratios, validation via IVDr) that the current preparation yields\nhigh-quality, interpretable spectra free from macromolecular interference.\n1. Validity: The experimental design for the AI analysis is rigorous and methodologically sound. The use of a blind validation\ncohort (n = 552) is a significant strength, and the data preprocessing and normalization steps are clearly described. Feature\nselection strategies were appropriately adapted to different data modalities. TabPFN’s performance was benchmarked\nagainst multiple algorithms (SVM, AutoGluon/XGBoost), and statistical evaluations were conducted using appropriate\nmetrics (AUC, sensitivity, specificity, and Wilcoxon test). The rationale for focusing on age and CA19-9 as the clinical\nvariables included in the final model is supported by clustering analysis (using Silhouette scores and UMAP), which\nindicates that they provide the highest discriminatory power. However, given that other variables, such as HbA1c, BMI, and\nfasting glucose, also show statistically significant differences between groups (Table 1), a brief clarification in the text would\nreinforce why these variables were ultimately excluded despite their apparent univariate significance.\n2. Significance: The work is highly significant and timely. It addresses a major challenge in oncology: the early detection of\nPDAC, a disease with a poor prognosis and no reliable screening test. The use of AI to integrate complex NMR\nmetabolomic signatures with minimal clinical data is innovative, and the demonstrated performance across disease stages\n(especially early-stage) is clinically meaningful. The minimal data requirement (n = 50 - 70) for robust prediction is especially\nimpactful for rare diseases.\n3. Data and Methodology: The NMR data acquisition and preprocessing pipeline is clearly described and appears to meet\nhigh standards in terms of automation and technical reproducibility (e.g., CV <1.4% for sample handling). The feature\nselection and machine learning workflow is transparent and technically well executed, and the use of SHAP analysis adds\ninterpretability to the model by identifying key biological signals.\nHowever, a major methodological flaw lies in the sample preparation protocol. The authors do not perform any protein\nremoval (e.g., via precipitation or ultrafiltration), which is a critical step in serum metabolomics. Without this, macromolecules\nsuch as albumin and globulins are retained in the sample, potentially distorting the ^1H NMR spectrum by line broadening\nand baseline interference. This can significantly affect the quantification of low-molecular-weight metabolites, even with the\nuse of CPMG sequences. This omission may undermine the reliability of the downstream analysis and classification\nperformance.\n4. Analytical Approach: The authors developed and compared multiple machine learning architectures, including a custom\nmultilayer SVM, AutoGluon ensemble models (e.g., XGBoost), and TabPFN, each trained using distinct feature selection\nstrategies tailored to the spectral characteristics of the NMR data. For example, SelectKBest was applied to NMR-M\nfeatures, and recursive feature elimination (RFE) was applied to NMR-M+L (pp. 9–10; Methods, lines 461–469). A reduced\nsubset of features (215 and 255, respectively) was used to accommodate the dimensional constraints of TabPFN (line 478).\nHyperparameter tuning was performed on the development set for the SVM and AutoGluon models, whereas TabPFN\nrequired no tuning, relying instead on its in-context learning framework, which was trained on synthetic priors (line 297- 299).\nThis TabPFN approach represents a novel transformer-based solution capable of producing accurate predictions from small\ndatasets (an essential strength in the context of rare cancers like PDAC). The authors evaluated model performance using\nstandard classification metrics (AUC, sensitivity, specificity, accuracy) and confidence intervals derived from bootstrap\nresampling (Table 2). However, the manuscript would benefit from the inclusion of additional metrics such as F1 scores,\nprecision-recall curves, or calibration plots, which are particularly valuable in diagnostic applications with class imbalance or\nvarying prevalence.\nSuggested Improvements\nExpand discussion on (i) generalizability across ethnicities and different patient cohorts. (ii) Limitations regarding metabolite\nidentification from NMR alone (e.g., peak overlap).\nClarify exclusion criteria for clinical features.\nConsider including or referencing external validation efforts (if any are planned) or providing discussion on feasibility.\nInclude full SHAP feature names in visualizations.\nExplicitly state which SHAP or statistical features were most relevant to early vs. late-stage detection (currently inferred but\nnot quantitatively ranked).\n5. Clarity and Context: The manuscript is clearly written, accessible to readers across disciplines, and well contextualized\nwith current literature. The introduction and discussion provide a balanced overview of the PDAC diagnostic landscape,\nhighlighting gaps and justifying the proposed approach. The figures are informative, although Figure 5 would benefit from\nenhanced visual clarity and explanation.\nImportantly, the figure captions are not clearly presented in the main body or as a standalone figure legend section, making it\ndifficult to interpret the figures independently of the text. For a manuscript of this complexity, full, self-contained figure\nlegends are essential to ensure clarity.\n6. References: The manuscript cites relevant and recent work, including foundational studies in metabolomics, AI-based\ndiagnostics, and PDAC biology. The references are comprehensive and appropriate.\n7. Expertise Scope: The evaluation was performed from the perspective of expertise in sample preparation, clinical\nmetabolomics, diagnostic model development, and translational oncology. While the reviewer did not independently verify\nthe TabPFN implementation or code, the methods appear to be appropriately adapted from the cited sources and aligned\nwith current best practices in AI model development. However, based on established protocols in serum metabolomics, a\nmajor methodological concern arises from the sample preparation workflow, which does not include any step for protein\nremoval (e.g., ultrafiltration or precipitation). This omission is likely to result in compromised spectral quality due to\nmacromolecular interference, raising questions about the reliability of downstream metabolite quantification and model\nperformance. This issue must be addressed for the study’s conclusions to be fully validated.", "reviewer_4": "PanMETAI: A High-Performance Tabular Foundation Model for Accurate Pancreatic Cancer Diagnosis via NMR\nMetabolomics\nWu et al, presenting a very nicely written manuscript, of a case control study (Pancreatic cancer vs healthy controls). The\naim of the work is to discover a novel pancreatic cancer biomarkers, based on NMR metabolomic profile and Machine\nlearning. Case-control: 902 serum samples, 478 cancer patients before treatment. 424 high risk patient group.\nThe novelty of the work sits within interpretation of complex NMR spectra utilising various deep learning methods, rather\nthan targeted biomarker quantification analysis.\nIntroduction: CA19.9 is not a standard biomarker for diagnosis, and is not recommended for screening by any guidelines.\nMetabolite are not stable over long period of time, how to be re assured that the storage of these samples were not affected\nover 20 years storage?\nHow many of the PDAC patients had Jaundice at time of sampling, as this is known to cause significant metabolite\ndegradation\nHow many PDAC patients were resectable at time of sampling,\nCA19.9 is known to be elevated in jaundice patients, therefore it’s inclusion in the model, although make statistical number\nappears better, but this is unlikely to be represented in the non jaundice early cancer patients. Suggest do a separate\nanalysis to non jaundice patients\nThe discovery and validation of the test done on internal re-validation. It would be preferable to have external re-validation\non independent samples.However, I appreciate that might be difficult to achieve because of lack of these samples\nlimitations in large-scale validation and clinical superiority over CA19-9 persist. The limitation is mostly related to lack of\nstudies testing these biomarkers in blood samples from early stages PDAC and at non symptomatic stages.\nPPV and NPV dependent on disease prevalence, therefore, can not be accurately calculated in case control studies. I\nsuggest this to be removed. and focus on AUC, Sensitivity and specificity\nAs these are cases of established cancer, CA19.9 likely not to be as effective in algorithms in early stages and non\nsymptomatic individuals as been seen previously. Although some reports suggest mild increase in CA19.9 might be an early\nchange as early as 2 years prior to symptomatic diagnosis, but this has not been proven in repeated reports\nTh authors need to shorten the discussion, and put a paragraph focusing on limitations of their approach and how easy to\nreproduce this analysis in a different lab setting,\nfurthermore, the strength of the study needs to be highlighted.\nI will be happy to review a revised manuscript if need to"}, "author_response": "Detailed response to reviewers’ comments\nReviewer #1 (Remarks to the Author):\nSummary:\nThis paper presents PanMETAI, a tabular foundation model based on TabPFN, developed for\nthe accurate and early detection of pancreatic ductal adenocarcinoma (PDAC) using ¹HNMR\nmetabolomics, clinical data (age, CA19-9), and the protein biomarker Activin A. The study\nencompasses 902 participants and employs a three-tier data division (training, development,\nand blind test) to build and validate models, including multilayer SVM, AutoGluon-XGBoost,\nand TabPFN. PanMETAI demonstrates outstanding diagnostic performance (AUC up to 0.99,\naccuracy ~94%), particularly in early-stage detection, and proves robust even with small\ndatasets. The study includes feature interpretability via SHAP analysis and metabolite\nquantification, positioning PanMETAI as a non-invasive, scalable diagnostic aid.\nResponse:\nWe appreciate the reviewer’s positive comments and encouragement.\nComments:\nComment 1. Positives:\n1- The paper impressively integrates NMR spectral features with clinical and biomarker data,\naddressing a real-world challenge in early cancer diagnostics.\n2- PanMETAI, leveraging TabPFN, exhibits high accuracy and robustness with small training\nsets, a notable advancement for low-incidence disease modelling.\n3- The inclusion of SHAP analysis and biochemical quantification adds transparency and\nscientific validity, enhancing translational relevance.\nResponse:\nWe appreciate the reviewer’s recognition.\nNegatives:\nComment 2.\n1. All data is derived from a single institution, limiting generalizability. Cross-center or\ninternational cohorts would strengthen the conclusions.\nResponse:\nWe thank the reviewer for this insightful comment. In response, we have expanded our\nvalidation to include an independent serum cohort collected from a geographically distinct\nEuropean population at the Lithuanian University of Health Sciences, to assess the model’s\ngeneralizability across different countries and centers. This external cohort (N= 322) comprised\n150 high-risk controls (HRC), 62 patients with early-stage pancreatic ductal adenocarcinoma\n(PDAC, stages I/II), and 110 patients with advanced-stage PDAC (stages III/IV).\nRemarkably, the PanMETAI model demonstrated robust predictive performance in this\nexternal international cohort, achieving an AUC of 0.93, 90% sensitivity, and 83% specificity\nin detecting patients with PDAC. This performance was consistently maintained in stage I/II\npatients, with an AUC of 0.91, 84% sensitivity, and 83% specificity (see New Table 3). These\nfindings not only underscore the reproducibility of the PanMETAI model across different\npopulations and institutions but also highlight its potential clinical utility for diagnosing PDAC\nin diverse populations with varying dietary habits in response to different real-world settings.\nCorresponding changes in the revised manuscript:\n● Page 3, Abstract section, Lines 83-87: We added the description of the European\nvalidation cohort performance.\n● Page 7, Results section, Lines 227-234: We added the description of the European\nvalidation cohort and the model performance of PanMETAI.\n● Page 10, Discussion, Lines 358-361: Discussion of the generalizability of PanMETAI\nmodel.\n● Revised Table 1: Characterization for PDAC and HRC in the 3rd external validation\ncohort from Lithuania.\n● New Table 3: List the performance metrics (AUC, sensitivity, specificity, F1 score) of\nthe PanMETAI model evaluated in the 3rd external Lithuania cohort.\nTable 1. Characteristic clinical demographic data for pancreatic ductal adenocarcinoma (PDAC)\npatients and high-risk controls (HRC) in the 1st exploration, 2nd validation, and 3rd external\nvalidation cohorts.\nTable 3. Diagnostic performance of PanMETAI in the external Lithuanian’s cohort (N=322).\nComment 3.\n2. While feature selection methods are described, the rationale for specific thresholds and their\nreproducibility across cohorts is not clearly justified.\nResponse:\nWe appreciate the reviewer's insightful question regarding our feature selection methodology\nand its reproducibility. We agree that the rationale behind feature selection thresholds is a\ncritical aspect of model development. We would like to clarify our approach and the underlying\nlogic behind it.\nOur study was meticulously designed with two distinct cohorts: the exploration cohort\n(Cohort 1) and the independent validation cohort (Cohort 2). We want to emphasize that\nCohort 2 was isolated entirely during the model development process, with no data from it used\nfor training, hyperparameter tuning, or feature selection. This strategy was specifically\nemployed to prevent data leakage and over-fitting, ensuring a robust, unbiased final validation.\nRationale for Feature Selection & Thresholds\nIn our machine learning pipeline, feature selection is viewed as an integral part of the overall\nmodel optimization process, rather than a separate, fixed step. The thresholds for feature\nselection (e.g., the number of features to select, K in SelectKBest) are treated as\nhyperparameters. The optimal number of features is determined by maximizing the model's\nperformance on the development dataset, specifically its performance on the developing test\nset. This approach ensures that we select the most informative and predictive features for our\nspecific modeling task.\nFor example, we used the Silhouette score to evaluate the clustering quality of the selected\nfeatures. The threshold (i.e., the number of features) that resulted in the highest Silhouette score\nwas chosen because it provided the best separation between the cancer and non-cancer groups.\nThis is a data-driven approach, not an arbitrary one.\nReproducibility Across Cohorts\nWe understand the reviewer's concern about reproducibility. However, reproducibility of the\nfeature selection process itself across different cohorts is not an expected outcome, nor is it the\nprimary goal. Each cohort, particularly those from diverse populations, may exhibit unique\nbiological and demographic characteristics. A well-designed feature selection pipeline should\nidentify the most relevant features for the data it is trained on.\nInstead, the accurate measure of our model's robustness and the quality of our chosen features\nlie in its predictive performance on an independent validation cohort. The fact that our\nmodel, trained in using features and thresholds optimized on Cohort 1, achieved excellent\nperformance on the completely unseen Cohort 2 already validates the robustness and\ngeneralizability of our selected features. It demonstrates that the metabolic and clinical\nsignatures identified in Cohort 1 are not specific to that cohort but are generalizable markers\nfor pancreatic cancer.\nComment 4. Major points:\n1. While TabPFN is presented as the foundation of the PanMETAI framework, the manuscript\nprovides limited justification for its selection over other high-performing tabular models such\nas TabNet or FT-Transformer.\n2. Although the results are impressive, a more critical discussion is warranted to contextualize\nTabPFN’s advantages—particularly its in-context learning paradigm—in relation to PDAC’s\nclinical constraints.\n3. The model’s parameter-free nature and efficiency are acknowledged, but the novelty would\nbe more compelling if benchmarked against a broader spectrum of foundation models,\nespecially those designed for biological datasets.\nResponse:\nWe thank the reviewer for the suggestions.\n(1 & 3) TabNet (Ref 1) was excluded due to its known sensitivity to hyperparameter settings,\nthe need for preprocessing (e.g., handling missing values via imputation), and comparatively\nslow tuning and training times relative to alternatives. It requires tuning of more than eight\nhyperparameters (25 formally), making it more suitable for medium to large datasets where the\nrisk of overfitting is lower. In contrast, TabPFN—designed for tabular classification on smaller\ndatasets—is pre-trained, requires no tuning, and has been shown to consistently outperform\nboosted tree models and neural methods (including TabNet) on OpenML benchmarks with\n≤ 1,000 samples (TabPFN paper), executing inference within seconds. Although TabNet offers\nfine-grained interpretability via attention masks, this was deemed insufficient to justify its\ninclusion, given TabPFN’s higher accuracy, native support for missing values, and\nsubstantially lower setup complexity.\nHowever, in order to further strengthen our benchmark, we chose to add the FT-transformer\n(Ref 2) into our benchmarks, as it requires fewer hyperparameters than TabNet; we didn’t find\na systematic comparison for the FT-transformer with TabPFN, and it is generally more stable\nand easier to tune than TabNet, especially on small datasets (Ref 2). We added a performance\ntest for the FT-transformer for our benchmark.\na. FT-transformer’s performance on Dev-Test\nModel Accuracy Sensitivity Specificity AUROC J-Statistic\nFT-transformer 0.94 0.96 0.88 0.96 0.84\n(0.89-0.97) (0.92-0.99) (0.78-0.97) (0.92-0.99) (0.74-0.93)\nTabPFN 0.96 0.98 0.90 0.98 0.88\n(0.92-0.99) (0.95-1.00) (0.81-0.98) (0.94-1.00) (0.78-0.96)\nRef:\n1. Arik, S.Ö. and Pfister, T., 2021, May. Tabnet: Attentive interpretable tabular learning. In Proceedings of\nthe AAAI conference on artificial intelligence (Vol. 35, No. 8, pp. 6679-6687).\n2. Gorishniy Y, Rubachev I, Khrulkov V, Babenko A. Revisiting deep learning models for tabular data.\nAdvances in neural information processing systems. 2021 Dec 6;34:18932-43.\n(2) TabPFN’s greatest strength lies in its in-context learning paradigm—the model is pretrained\nonce on millions of synthetic tabular datasets (simulating missingness, noise, and causal\nvariability), and makes Bayesian-style predictions in a single forward pass when fed both\ntraining and test data, without per-cohort fine-tuning or hyperparameter search. This makes it\nparticularly well-suited for PDAC clinical datasets, which are often small (< 10 k patients),\nheterogeneous, and derived from multiple centers with different preprocessing pipelines, as\nTabPFN dynamically adapts to each dataset at inference time. Furthermore, emerging\nextensions, such as Drift-Resilient TabPFN (Ref. 1), demonstrate empirical robustness to inter-\nsite and temporal distributional shifts commonly seen in multi-institutional cohorts. Despite\nlacking inherent interpretability, TabPFN supports post-hoc attribution methods such as SHAP\nand LOCO, which can be applied directly during inference without retraining (Ref 2).\nRef:\n1. Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter, Drift-Resilient\nTabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data, NeurIPS 2024\n(https://openreview.net/forum?id=p3tSEFMwpG)\n2. Rundel, D., Kobialka, J., von Crailsheim, C., Feurer, M., Nagler, T., Rügamer, D. (2024).\nInterpretable Machine Learning for TabPFN. In: Longo, L., Lapuschkin, S., Seifert, C. (eds)\nExplainable Artificial Intelligence. xAI 2024. Communications in Computer and Information\nScience, vol 2154. Springer, Cham. https://doi.org/10.1007/978-3-031-63797-1_23\n(https://arxiv.org/abs/2403.10923)\nCorresponding changes in the revised manuscript:\n● Revised Supplemental Table 1: Added performance comparison for FT-transformer\nin our benchmark.\n● Page 11-12, Discussion section, Lines 401-410: Add a more critical discussion of\nTabPFN’s advantages.\nSupplemental Table 1. Performances between different models in the Dev-test dataset.\nComment 5.\n4. The study highlights metabolite-level changes and uses SHAP for feature attribution, but it\nstops short of performing deeper biological pathway analysis or metabolic network\ninterpretation. Mapping the identified features to disrupted biochemical pathways (e.g.,\nglutaminolysis, glycolysis, lipid remodeling) could significantly enhance the translational\nrelevance of the findings. Without this layer of biological insight, the model’s interpretability,\nwhile technically sound, remains disconnected from systems-level understanding of PDAC\nprogression.\nResponse:\nWe thank the reviewer for this valuable suggestion. In response, we have enhanced the\nbiological interpretability of our model by mapping SHAP-identified PDAC features to\ncorresponding metabolites and conducting metabolic pathway and network enrichment\nanalyses. Specifically:\na. Feature Annotation: We have meticulously mapped the SHAP-identified spectral\nfeatures to their corresponding metabolite names (detailed in Supplemental Table 3).\nThis step provides a clear biochemical context, enabling us to track metabolite\nconcentration trends across early and late stages of PDAC.\nb. Pathway and Network Analysis: We conducted comprehensive pathway, metabolic\nnetwork, and disease/biofunction enrichment analyses using MetaboAnalyst 6.0 and\nIngenuity Pathway Analysis (IPA), as illustrated in the new Figure 5. This multi-layered\nanalysis allowed us to successfully map the SHAP-ranked metabolites to key, well-\ndocumented dysregulated metabolic pathways in pancreatic cancer, including:\nGlucose-Alanine Cycle, Phenylalanine and Tyrosine Metabolism, Aspartate Metabolism,\nWarburg Effect Signaling Pathway (Glycolysis), and others.\nThese findings substantially increase the biological interpretability of our model and strengthen\nits translational relevance by definitively linking the observed metabolomic alterations to\nknown mechanisms of tumor progression in PDAC.\nChanges in manuscript:\n● Page 8-9, Results section, Lines 291-305: Description of pathway/network analysis of\nSHAP-identified features to interpretate the potential biological mechanisms beyond\nPDAC.\n● New Supplemental Table 3: Annotated SHAP plot with corresponding metabolites\nand feature trends.\n● Move original Supplemental Table 3 to Supplemental Table 4.\n● New Figure 5A-D and Figure legend (Page 23, Lines 816-835): Metabolic pathway,\nnetwork, and disease and function analyses visualizations for SHAP-identified\nmetabolic features using MetaboAnalyst 6.0 and IPA software.\n● Move original Figure 5 to Figure6.\nWe believe this addition lays the groundwork for future studies to investigate the tumor-\nspecific metabolic microenvironment in PDAC, significantly enhancing the systems-level\nunderstanding and translating impact of our findings.\nSupplemental Table 3. SHAP Feature Annotation with Metabolite Assignments and Stage-\nDependent Trends in Pancreatic Cancer.\nFigure 5. Pathway and network analysis of SHAP-identified serum metabolites in PDAC.\n(A) Metabolic Pathway Enrichment Map: Pathway analysis performed using MetaboAnalyst 6.0. The\ngraph shows metabolic pathways significantly enriched by SHAP-ranked metabolites (e.g., 3-\nAminoisobutanoic acid, Glucose, Taurine, TMAO). The y-axis represents statistical significance (-\nlog (p-value)), while the x-axis indicates pathway impact (based on topological analysis, reflected by\n10\nbubble size). Bubble color signifies statistical significance, ranging from red (highly significant) to\nwhite (non-significant). (B) Metabolic Interaction Network: A network generated by Ingenuity Pathway\nAnalysis (IPA) illustrating interactions within Carbohydrate Metabolism, Molecular Transport, and\nSmall Molecule Biochemistry. Red-colored nodes represent molecules found to be increased in PDAC\npatients (lighter colors indicate a less elevated trend), while white nodes have no directional prediction.\nSolid lines represent direct interactions, and dashed lines represent inferred indirect interactions. Arrows\nindicate the directionality of molecular influence or regulation. (C) Disease and Biofunctions Summary:\nThe top 20 disease and functional categories identified by IPA based on the SHAP-identified\nmetabolites, presented by the -log (p-value) of enrichment. This validates the biological relevance of\n10\nthe selected features by linking them to hallmark themes in cancer progression, organismal injury, and\nlipid metabolism.\nComment 6.\n5. Several methodological gaps limit the robustness and generalizability of the model. First, no\nablation study is performed to assess the individual contribution of each data modality or\nfeature subset, which is crucial for clinical simplification. Second, although the data spans\nnearly two decades (2004–2022), the analysis does not investigate how diagnostic markers or\nmodel performance may vary over time, missing an opportunity to explore cohort drift or\nbiomarker stability. Lastly, external validation using data from other institutions or populations\nis absent, significantly limiting clinical readiness claims.\nResponse:\nFirst– Ablation study not performed\nWe appreciate the reviewer’s suggestion regarding ablation analysis. As shown in\nSupplemental Table 2, we conducted an ablation study to evaluate the relative contribution of\nindividual data modalities (NMR metabolomics-only vs. protein and clinical-only vs. 3\ncombinations) for both early- and late-stage analyses. Results revealed that the metabolomics\ndata alone or clinical/protein data alone already achieved an acceptable diagnostic performance\n(AUC >0.8 in early-stage and AUC > 0.9 in late-stage), while the integration with\nmetabolomics, protein (Activin A), and clinical features (CA19-9) further enhanced robustness\nand especially improved the sensitivity in the stage I/II of PDAC prediction. This finding\nsupports the essential of combining both up-down omics study (from protein to metabolomics\ndata) to enhance early diagnosis in real-world settings.\nSupplemental Table 2. TabPFN Blind Test Performance on early and late stage PDAC.\nSecond – Lack of temporal robustness analysis\nWe acknowledge the importance of evaluating temporal stability across a long recruitment\nperiod (2004–2022). Due to the retrospective nature of sample collection, our HRC (healthy\nrisk control) and PDAC groups were recruited in parallel over similar time frames, spanning\nfrom 10-20 years. While direct year-by-year stratification was limited by batch size, this similar\ntime frame recruitment strategy reduces confounding due to cohort drift.\nSince our PDAC patients were collected from 2005 to 2022, we perform a 2D-PCA on the\ndiagnostic markers of PDAC patients, focusing on the beginning (2005-2007) and the end\n(2020-2022), to compare for temporal shifts. (New Supplemental Figure 8). On the PCA,\nthere are no significant shifts between 2005-2007 and 2020-2022. Moreover, we also tested the\nsensitivity (accuracy among these PDAC patients) across all 18 years, and no significant impact\nwas observed in our model’s performance. These findings suggest that the features employed\nby the PanMETAI model are temporally stable.\nWe appreciate the suggestion of exploring cohort drift or biomarker stability, which could be\na future goal following an investigation of the addressed metabolites from SHAP analysis.\nChanges made in manuscript:\n● Page 11, Discussion section, Lines 388-393: Discuss sampling timeframe and\nevaluating strategy of biomarkers drift temporally.\n● New Supplemental Figure 9: (a) 2D-PCA plot on diagnostic features employed by\nPanMETAI, grouped by sample collection years between the beginning (2005-2007)\nand ending (2020-2022). (b) Model performance across PDAC patients from 2005 to\n2022.\nSupplemental Figure 9. Temporal stability of metabolic features selected by the PanMETAI\nmodel. (a) 2D-PCA plot on diagnostic features employed by PanMETAI, grouped by sample collection\nyears between the beginning (2005-2007) and ending (2020-2022). (b) Model performance across\nPDAC patients from 2005 to 2022.\nLastly – Lack of external validation\nIn line with the reviewer’s concern regarding generalizability, we actively collaborated with\nProfessor Juozas Kupcinskas, MD, PhD, Head of the Gastroenterology Department and\nInstitute for Digestive Research to expand our validation by recruiting an independent external\ncohort of 150 HRC and 150 PDAC patients (62 Stage I/II and 88 Stage III/IV) from a separate\ninstitution in Lithuania, located in a different geographical region as description in Comment\n2’ response. PanMETAI demonstrated consistent performance, achieving an AUC of 0.93,\n90% sensitivity, and 83% specificity in the diagnosis of PDAC in this external validation cohort.\n(New Table 3)\nThis external validation robustly confirms model reproducibility across institutions and\npopulations, further supporting the translational readiness of PanMETAI for real-world PDAC\nscreening.\nTable 3. Diagnostic performance of PanMETAI in the external Lithuanian’s cohort (N=322).\nReviewer #2 (Remarks to the Author):\nComment 1. The authors use an MR spec serum signature of metabolites and lipoproteins\nalong withCA19-9 and Activin A as a test for pancreatic cancer and show that it achieves high\nperformance in a validation set.\nResponse:\nWe appreciate the reviewer’s recognition.\nComment 2. One limitation of the study is the age difference between the cases and controls,\nwith a mean of ~15 years. The controls were patients with familial/genetic risk. Although this\ndifference is not likely to dramatically affect the results, certain metabolic alterations increase\nwith age, such as impaired glucose tolerance/DM and associated changes in lipids that could\nmodify the prediction.\nResponse:\nWe thank the reviewer for highlighting the age difference between the case and high-risk\ncontrol (HRC) cohorts. We agree that while this age difference is common in cancer screening\nstudies, it could potentially introduce bias related to age-dependent metabolic changes (e.g.,\nimpaired glucose tolerance and altered lipid profiles).\nTo definitively demonstrate that our model's performance is independent of age, BMI, and\nsmoking status, we performed a dedicated sub-analysis.\na. Sub-Cohort Selection: We created a subpopulation from Cohort 2 (the independent\nvalidation cohort) where there were no significant differences in age, BMI, or smoking\nstatus between the PDAC cases and the HRCs (new Supplemental Table 5 and 6).\nb. Performance Test: We re-tested the PanMETAI model's performance specifically on this\nbias-controlled sub-cohort.\na. TabPFN’s performance on the sub-cohort with no significant age, BMI, and smoking differences\nbetween cases and controls.\nModel Accuracy Sensitivity Specificity AUROC\nTabPFN 0.94 0.93 0.94 0.99\n(N=529) (0.91-0.95) (0.89-0.96) (0.91-0.97) (0.98-0.99)\nTabPFN 0.91 0.92 0.89 0.97\n(N=391; w/o age, BMI, (0.88-0.94) (0.89-0.96) (0.84-0.94) (0.96-0.99)\nsmoke differences)\nb. Numbers of predicted negatives and positives within each stage.\nModel Stage Predicted Negatives Predicted Positives\nTabPFN HRC 136 17\n(N=391; w/o age, BMI, PDAC stage I 3 9\nsmoke differences) PDAC stage II 5 20\nPDAC stage III 4 30\nPDAC stage IV 7 160\nThe AUC remained exceptionally high at 0.97, indicating that controlling for these common\nconfounders only resulted in a marginal, non-significant drop in performance. This analysis\nstrongly supports our conclusion that the predictive power of PanMETAI is primarily driven\nby the underlying metabolic and protein signatures of PDAC, rather than by demographic\nfactors such as age, BMI, and smoking status.\nChanges made in manuscript:\n● New Supplemental Table 5: Characteristics of study participants by PDAC status in a\n2nd validation cohort adjusted for age, BMI, and smoking status (N = 391).\n● New Supplemental Table 6: TabPFN’s performance on the sub-cohort with no\nsignificant age, BMI, and smoking differences between cases and controls.\n● Page 11, Discussion section, Lines 380-384: Discuss model performance after\nadjusting for age, BMI, and smoke status in a blind test set of cohort 2.\nSupplemental Table 5 Characteristics of study participants by PDAC status in a 2nd validation\ncohort adjusted for age, BMI, and smoking status (N = 391), and balanced representation of early-\nand late-stage patients (N=227).\nSupplemental Table 6 TabPFN’s performance on sub-cohort 2, with no significant age, BMI,\nand smoking differences between cases and controls.\nComment 3. In addition, the number of stage I/II PDAC cases in the validation set is also quite\nsmall compared to the number of Stage III/IV cases; 37 vs 241. Therefore, the diagnostic\nperformance of the model is driven by advanced-stage cases, which is not an adequate test of\nmodel performance.\nResponse:\nWe thank the reviewer for pointing out the imbalance in cancer staging within our validation\nset (37 Stage I/II vs. 241 Stage III/IV) and for noting that this distribution may overemphasize\nthe model’s performance on advanced-stage cases.\nTo robustly assess the model's actual performance across different disease stages, we\nperformed a sensitivity analysis by constructing a balanced subpopulation from the original\nvalidation cohort.\na. Balanced Sub-Cohort Creation: We created a sub-cohort by balancing the early-stage and\nlate-stage PDAC cases (n=37 Stage I/II and n=37 Stage III/IV). The original number of High-\nRisk Controls (HRCs) was retained in this analysis.\nb. Performance Test: We re-tested the PanMETAI model's performance on this balanced sub-\ncohort.\na. TabPFN’s performance on sub-cohort with balanced numbers of early (stage I/II) and late (stage\nIII/IV) cases.\nModel Accuracy Sensitivity Specificity AUC\nTabPFN 0.94 0.93 0.94 0.99\n(N=529) (0.91-0.95) (0.89-0.96) (0.91-0.97) (0.98-0.99)\nTabPFN 0.89 0.88 0.89 0.96\n(N=227; w/ balanced (0.84-0.93) (0.81-0.95) (0.83-0.93) (0.92-0.98)\nearly- and late-stages\npatients and w/o age,\nBMI, smok differences)\nb. Numbers of predicted negatives and positives within each stage.\nModel Stage Predicted Negatives Predicted Positives\nTabPFN HRC 136 17\nPDAC stage I 3 9\nPDAC stage II 5 20\nPDAC stage III 1 4\nPDAC stage IV 0 32\nThe results confirm that the model's performance remains strong, even when the influence of\nadvanced cases is intentionally reduced. The resulting AUC of 0.96 on the balanced sub-cohort,\ndespite the smaller sample size, demonstrates that advanced-stage signatures do not solely\ndrive the model and maintain robust diagnostic capability across the cancer spectrum. We\nacknowledge that the ideal test would utilize a larger pool of early-stage patients. We plan to\nfocus our future recruitment efforts on expanding the number of Stage I/II cases for definitive\nvalidation in subsequent studies.\nChanges made in manuscript:\n● New Supplemental Table 5: Characteristics of study participants by PDAC status in a\n2nd validation cohort adjusted for age, BMI, and smoking status with a balanced\nnumber of early- and late-stage patients (N=227).\n● New Supplemental Table 7: TabPFN’s performance on sub-cohort with no significant\nage, BMI, and smoking differences with a balanced number of early (stage I/II) and late\n(stage III/IV) cases\n● Page 11, Discussion section, Lines 380-384: Discuss model performance after\nadjusting for age, BMI, and smoke status with balanced number of early and late PDAC\ncases in a blind test set of cohort2.\nSupplemental Table 5 Characteristics of study participants by PDAC status in a 2nd validation\ncohort adjusted for age, BMI, and smoking status (N = 391), and a balanced representation of\nearly- and late-stage patients (N=227).\nSupplemental Table 7. TabPFN’s performance on sub-cohort 2 with no significant age, BMI,\nand smoking differences with a balanced number of early (stage I/II) and late (stage III/IV)\ncases\nReviewer #3 (Remarks to the Author):\nComment 1. This study presents PanMETAI, a transformer-based AI model (TabPFN) that\nintegrates NMR metabolomics, clinical data (including age and CA19-9), and protein\nbiomarkers (Activin A) for the diagnosis of pancreatic ductal adenocarcinoma (PDAC). Using\n902 serum samples from high-risk controls and PDAC patients across different stages, the\nmodel achieved outstanding performance (AUC 0.99, accuracy 0.94) on a blinded validation\ncohort. Notably, PanMETAI improved early-stage detection and remained robust with small\nsample sizes, highlighting its potential for clinical deployment in low-incidence cancer\ndiagnostics.\nResponse:\nWe appreciate the reviewer's positive summary and recognition of our core findings.\nComment 2. The choice of NMR as the primary metabolomics platform, while well justified\nin terms of throughput and quantitative reproducibility, may limit the detection of low-\nabundance yet biologically relevant metabolites due to its lower sensitivity and narrower\ndynamic range compared to high-resolution mass spectrometry (HRMS). Although the authors\nbriefly mention some challenges associated with MS, it would strengthen the manuscript to\nprovide a more balanced discussion. HRMS-based metabolomics, particularly in targeted and\nsemi-targeted formats, has evolved significantly, with robust standardization protocols, high\nanalytical sensitivity, and broad metabolite coverage. It remains a leading platform for the\ndiscovery of cancer biomarkers. The authors should clarify why HRMS was omitted and\nwhether future iterations of PanMETAI might integrate MS-based data to further enhance the\nmodel’s diagnostic utility.\nResponse:\nWe sincerely appreciate the reviewer’s insightful comments. We agree that HRMS-based\nmetabolomics is a powerful platform for detecting low-abundance metabolites and has indeed\nadvanced significantly in standardization, sensitivity, and metabolome coverage. However, in\nthe context of clinical biomarker discovery and model translation, we prioritized NMR\nspectroscopy due to its superior reproducibility, throughput, and quantitative consistency,\nparticularly critical in large-scale cohort studies. Reproducibility remains a key factor for\nbiomarker reliability and clinical applicability.\nFurthermore, our whole-spectrum profiling strategy with NMR does not rely solely on\nmetabolite identification or quantification. Instead, it leverages the entire spectral feature space,\nincluding those signals below quantification limits but over the limit of detection. This\napproach allows effective utilization of all available biochemical signals for machine learning-\nbased model training, enhancing the coverage of NMR-based metabolomics.\nNonetheless, we fully acknowledge the complementary value of MS-based platforms. In fact,\nwe are actively pursuing HRMS-based quantitative metabolomics to identify additional\ncandidate metabolites for PDAC detection. However, we have encountered substantial\nchallenges in signal reproducibility, particularly for those small metabolites across datasets,\nwhich currently hinders direct integration into diagnostic models.\nIn line with the reviewer’s suggestion and inspired by literature reporting selected lipid species,\nsuch as very long-chain sphingomyelins, ceramides, and (lyso)phosphatidylcholines, as\npromising diagnostic biomarkers (Wolrab, D., et al., Ref 1) for pancreatic cancer. Therefore,\nwe plan to integrate HRMS-based lipidomics in future iterations of PanMETAI to enhance\nmodel performance potentially. This future direction for integration of HRMS-based\nlipidomics has now been included in the revised Discussion section to reflect its importance.\nRef:\n1. Wolrab, D., Jirásko, R., Cífková, E. et al. Lipidomic profiling of human serum enables detection of\npancreatic cancer. Nat Commun 13, 124 (2022). https://doi.org/10.1038/s41467-021-27765-9.\nCorresponding changes in the revised manuscript:\n● Page 12, Discussion section, Lines 418-425: Include future direction for integration of\nHRMS-based lipidomics.\nComment 3. Major Methodological Concern: Sample Preparation for NMR\nSpectroscopy\nA critical limitation of this study lies in the serum sample preparation protocol used for^1H\nNMR metabolomics. The manuscript describes a direct dilution of native serum with phosphate\nbuffer in D₂O (110 μL + 110 μL), but fails to address the presence of intact serum proteins such\nas albumin and globulins. These macromolecules dominate the NMR spectrum and broaden\nsignals of low-molecular-weight metabolites through T₂ relaxation and line broadening effects.\nWithout protein removal, either by organic solvent precipitation (e.g., methanol or acetonitrile)\nor ultrafiltration (e.g., with a 3 kDa or 10 kDa cutoff), the spectra are likely compromised,\nthereby undermining the accuracy and sensitivity of metabolite detection. Although the authors\nmention using CPMG sequences for metabolite detection, this is not a substitute for protein\nremoval. CPMG suppresses broad macromolecular signals but does not eliminate their\ninterference, especially in crowded regions of the spectrum or when quantification is required.\nFurther, the use of a 1:1 dilution with phosphate buffer introduces significant concerns:•\nDilution reduces signal intensity, particularly affecting low-abundance biomarkers.• Na₂HPO₄\ncan alter the chemical environment of pH-sensitive metabolites (e.g., citrate, lactate).• The\npreparation does not eliminate water, despite D₂O use. Without a detailed description of\npresaturation parameters, water suppression quality, or the management of residual water\nsignals, it is difficult to assess the reliability of the spectra.\nResponse:\nWe appreciate the reviewer’s valuable comment regarding serum sample preparation. While\nprotein precipitation or ultrafiltration is commonly employed in certain metabolomics\nworkflows, our NMR-based approach follows a well-established and validated protocol that\nretains serum macromolecules. Specifically, the method aligns with the standardized\nprocedures recommended by Bruker IVDr, which are widely adopted in large-scale NMR\nmetabolomics and diagnostic studies. This protocol has been consistently shown to yield robust\nand reproducible quantification of low-molecular-weight metabolites even in the presence of\nmacromolecules, particularly when using CPMG pulse sequences to suppress broad signals\nfrom proteins and lipoproteins. Importantly, this approach has been validated in high-impact\nstudies across diverse disease areas, including:\n1. Zhou et al., Adv Sci, 2024 (Ref 1)\n2. Karjalainen et al., Nature, 2024 (Ref2)\n3. Fromentin et al., Nat Med, 2022 (Ref3)\nAccording to Bruker’s technical report, this protocol has been applied in over 230 peer-\nreviewed publications across various disease areas since 2017.\nIn addition, another advantage of preserving serum protein content allows us to perform\nsimultaneous analysis of lipoprotein subclass profiles (NMR-M+L spectra), an important\nfeature for pancreatic cancer research. (Ref 4) Lastly, retaining macromolecules also facilitates\ndirect spectral comparison and data integration with other research centers that employing the\nsame NMR methodology.\nWe agree that CPMG is not a complete substitute for physical protein removal, but in the\ncontext of standardized and harmonized NMR-based diagnostics, our approach provides a\npractical and validated balance between metabolomic resolution and clinical feasibility.\nRegarding water suppression and spectral quality:\nAll acquired spectra passed Buker IVDr QC criteria for experiment validation, including water\nsuppression quality, as shown in the attached report.\nFor handling residual water signals in PanMETAI diagnosis, we excluded the spectral region\nfrom 4.64 ppm to 4.78 ppm for downstream machine learning analyses. This procedure is\ndescribed under “Data processing of 1H NMR spectra for ML” in the Materials and\nMethods section (Page 14, Lines 497-499).\nBuker IVDr QC for experiment validation\nData processing of 1H NMR spectra for ML\nTwo types of spectra (NMR-M & -M + L) were measured, one is metabolite-specific (NMR-M) and\nanother, lipoprotein-containing (NMR-M + L). The NMR spectra were read using the nmrglue module50.\nA Fourier-transformed NMR spectrum contains a list of signal intensities corresponding to the 1H\nchemical shifts. After acquiring the reads, each spectrum was normalized by its internal standard TSP-\nd for handling bench differences. The normalized spectrum was further trimmed for removing regions\n4\nthat do not contain meaningful information: for NMR-M, the data with frequencies lower than or equal\nto 0.13 ppm, or higher than or equal to 11.13 ppm were removed; for NMR-M + L, lower than or equal\nto 0.11ppm or higher than or equal to 10.00 ppm were trimmed; for both NMR spectra, data between\n4.64 ppm and 4.78 ppm was removed due to the water signal. After normalization and trimming, the\nresulting spectra were used as inputs for the following ML pipeline.\nRef:\n1. Zhou Z., et al. VLDL and LDL Subfractions Enhance the Risk Stratification of Individuals Who\nUnderwent Epstein-Barr Virus-Based Screening for Nasopharyngeal Carcinoma: A Multicenter\nCohort Study. Adv Sci (Weinh). 2024 Jun;11(22). https://doi.org/10.1002/advs.202308765.\n2. Karjalainen, M.K., Karthikeyan, S., Oliver-Williams, C. et al. Genome-wide characterization of\ncirculating metabolic biomarkers. Nature 628, 130–138 (2024). https://doi.org/10.1038/s41586-\n024-07148-y.\n3. Fromentin, S., Forslund, S.K., Chechi, K. et al. Microbiome and metabolome features of the\ncardiometabolic disease spectrum. Nat Med 28, 303–314 (2022). https://doi.org/10.1038/s41591-\n022-01688-4.\n4. Bae G, Berezhnoy G, Flores A, Cannet C, Schäfer H, Dahlke MH, Michl P, Löffler MW,\nKönigsrainer A, Trautwein C. Quantitative Metabolomics and Lipoprotein Analysis of PDAC\nPatients Suggests Serum Marker Categories for Pancreatic Function, Pancreatectomy, Cancer\nMetabolism, and Systemic Disturbances. J Proteome Res. 2024 Apr 5;23(4):1249-1262. doi:\n10.1021/acs.jproteome.3c00611.\nComment 4. This lack of standard protein-removal procedures represents a fundamental\nmethodological flaw. Without addressing this issue, the validity of the downstream machine\nlearning models, however robust, remains questionable, as they are ultimately trained on\nspectra that may be distorted or biased by macromolecular contributions. Recommendation:\nThe authors must either (1) repeat the NMR acquisition using appropriate protein-removal\nprotocols or (2) provide quantitative evidence (e.g., metabolite linewidths, S/N ratios,\nvalidation via IVDr) that the current preparation yields high-quality, interpretable spectra free\nfrom macromolecular interference.\nResponse:\nWe thank the reviewer for raising this important methodological point. All serum samples in\nour study were processed and acquired in alignment with the Bruker IVDr protocol, which is\nwidely accepted in clinical NMR metabolomics. This includes:\n● Sample Preparation: Serum samples were diluted 1:1 with phosphate buffer in D2O,\nfollowing the Bruker IVD SOP without protein precipitation or ultrafiltration, as\nsupported by experiment validation (see references in prior Response 3).\n● Spectral Validation via IVDr: All spectra were subjected to Bruker IVDr validation;\nonly spectra that passed IVDr validation criteria were included for full-spectra machine\nlearning analyses (see an example in the attached figure).\n● Quantitative Spectral Quality Metrics: Metabolite (Alanine) linewidths were set to\n< 1.5 Hz, consistent with IVDr. For the lowest-intensity metabolite peaks selected by\nPanMETAI, signal-to-noise (S/N) ratios were confirmed to be >3, as demonstrated in\nthe representative spectrum. These metrics affirm the high reliability of the spectral\ninput.\nSpectral validation via IVDr\n(A) (B)\nRepresentative spectra illustrate the S/N ratios of PanMETAI-selected spectral features in Figure\n4A. (A) Features with the lowest and (B) the highest signal intensity.\nCorresponding changes in the manuscript:\n● For manuscript clarity, we include additional information under “1H NMR acquisition”\nin the Materials and Methods section (Page 13-14, Lines 478-482).\n1H NMR acquisition\nThe 1H NMR spectra were acquired using a 600 MHz Bruker Avance III HD spectrometer equipped\nwith a 5 mm BBI probe and fitted with the Bruker SampleJet robot cooling system set to 4°C. A standard\none-dimensional (1D) pulse sequence with solvent presaturation experiment included both small\nmolecule metabolites and lipoproteins (32 scans, zero-filled to 128K refers to NMR-M + L spectrum),\nwhereas a Carr−Purcell−Meiboom−Gill spin−echo experiment was used to identify small molecule\nmetabolites (32 scans, zero-filled to 128K refers to NMR-M spectrum). The data were processed in\nautomation using a Bruker Topspin 3·6·2 and ICON NMR to achieve phasing and baseline correction.\nThe TSP-d provided a chemical shift reference (1H, δ 0·00). All spectra passed the Bruker in vitro\n4\ndiagnostics research (IVDr) validation criteria—covering water suppression quality, line width (≤\n1.50 Hz for reference metabolite, Alanine), and shim—were included for downstream machine learning\nanalysis.\nThe quantification of serum metabolites and lipoproteins were completed using the Bruker IVDr\nmethods. A total of 39 metabolites and 112 lipoprotein parameters were generated using the IVDr\nQuantification in Plasma/Serum (B.I.Quant-PS) and Lipoprotein Subclass Analysis (B.I.LISA)\nmethods47.\nComment 5. 1. Validity: The experimental design for the AI analysis is rigorous and\nmethodologically sound. The use of a blind validation cohort (n = 552) is a significant strength,\nand the data preprocessing and normalization steps are clearly described. Feature selection\nstrategies were appropriately adapted to different data modalities. TabPFN’s performance was\nbenchmarked against multiple algorithms (SVM, AutoGluon/ XGBoost), and statistical\nevaluations were conducted using appropriate metrics (AUC, sensitivity, specificity, and\nWilcoxon test). The rationale for focusing on age and CA19-9 as the clinical variables included\nin the final model is supported by clustering analysis (using Silhouette scores and UMAP),\nwhich indicates that they provide the highest discriminatory power. However, given that other\nvariables, such as HbA1c, BMI, and fasting glucose, also show statistically significant\ndifferences between groups (Table 1), a brief clarification in the text would reinforce why these\nvariables were ultimately excluded despite their apparent univariate significance.\nResponse:\nWe appreciate the reviewer's detailed attention to our feature selection process. This is an\nimportant point for clarifying the distinction between raw statistical significance and utility in\na predictive model. Although variables like HbA1c, BMI, and fasting glucose showed\nstatistically significant differences between groups (as seen in Table 1), they were ultimately\nexcluded from the final PanMETAI model for two primary, methodologically sound reasons:\na. Data Completeness and Clinical Monitoring Standard: The high rate of missing or\nincomplete data for HbA1c, BMI, and fasting glucose within our broader clinical records made\nthem unsuitable for reliable feature inclusion across the entire cohort. We prioritized Age and\nCA19-9 because they are robustly and consistently collected as standard monitoring parameters\nfor PDAC, thus ensuring maximum data completeness.\nb. Fair Benchmarking of AI Models: Excluding features with high missingness was\nnecessary to establish a fair baseline for comparing TabPFN against traditional machine\nlearning methods (such as SVM and XGBoost/AutoGluon). While TabPFN inherently has\ncapabilities to handle missing data, most benchmark models do not. Relying on incomplete\ndata would have unfairly compromised the performance of our comparison models, thereby\nundermining the rigor of our entire benchmarking effort.\nIn summary, the decision to rely exclusively on Age and CA19-9 was justified not only by the\nsuperior discriminant power observed in the UMAP/Silhouette analysis but also by the\ncritical need for data completeness to maintain methodological integrity when validating the\nTabPFN platform.\nTable 1. Characteristic clinical demographic data for pancreatic ductal adenocarcinoma (PDAC)\npatients and high-risk controls (HRC) in the 1st exploration.\nComment 6. 2. Significance: The work is highly significant and timely. It addresses a major\nchallenge in oncology: the early detection of PDAC, a disease with a poor prognosis and no\nreliable screening test. The use of AI to integrate complex NMR metabolomic signatures with\nminimal clinical data is innovative, and the demonstrated performance across disease stages\n(especially early-stage) is clinically meaningful. The minimal data requirement (n =50 - 70) for\nrobust prediction is especially impactful for rare diseases.\nResponse:\nWe appreciate the reviewer’s positive comments and encouragement.\nComment 7. 3. Data and Methodology: The NMR data acquisition and preprocessing pipeline\nis clearly described and appears to meet high standards in terms of automation and technical\nreproducibility (e.g., CV <1.4% for sample handling). The feature selection and machine\nlearning workflow is transparent and technically well executed, and the use of SHAP analysis\nadds interpretability to the model by identifying key biological signals.\nHowever, a major methodological flaw lies in the sample preparation protocol. The authors do\nnot perform any protein removal (e.g., via precipitation or ultrafiltration), which is acritical\nstep in serum metabolomics. Without this, macromolecules such as albumin and globulins are\nretained in the sample, potentially distorting the ^1H NMR spectrum by line broadening and\nbaseline interference. This can significantly affect the quantification of low-molecular-weight\nmetabolites, even with the use of CPMG sequences. This omission may undermine the\nreliability of the downstream analysis and classification performance\nResponse:\nWe thank the reviewer for the critical evaluation of our methodology and for highlighting the\nimportance of protein removal in serum metabolomics.\nWe want to clarify that our sample preparation strategy was intentional and adheres to\nestablished protocols for clinical metabolomics studies, specifically those designed for large-\nscale, high-throughput diagnostic applications.\na. Clinical Rationale (IVD-Grade Protocol): Our study intentionally employs a minimal\nsample preparation approach that aligns with IVD-grade (In Vitro Diagnostic) clinical NMR\nprotocols (specifically the Bruker IVDr platform). These protocols are standardized to\nmaximize reproducibility and retain the maximal biological information present in the serum,\nincluding signals from lipoproteins and larger molecules. Removing all proteins, while\nsimplifying the spectrum, would eliminate valuable diagnostic information related to\nlipoprotein profiles that we utilize in our multi-modal approach.\nb. Addressing Macromolecular Interference: We recognize that macromolecules (like\nalbumin) can interfere with small-molecule metabolite quantification. However, this\ninterference is mitigated by two key technical factors:\nCPMG Pulse Sequences: We utilized the Carr-Purcell-Meiboom-Gill (CPMG) pulse\nsequence for the NMR-M (small metabolite) spectrum. This sequence effectively suppresses\nthe broad signals from macromolecules, thereby isolating and enhancing the signals from the\nsmall, mobile metabolites.\nRigorous QC: Our data underwent stringent quality control checks, including those validated\nby the IVDr platform, which cover essential parameters such as metabolite linewidths and\nsignal-to-noise ratios to support the efficiency of CPMG pulse sequences.\nThe successful performance of our model, benchmarked against external validation cohorts and\nsupported by robust QC data (e.g., CV < 1.4% for handling), indicates that our methodological\nchoices did not compromise the reliability of the downstream classification performance.\nThis minimal preparation strategy is a methodological feature that enhances the translational\npotential of PanMETAI, as it enables rapid and standardized screening in a clinical setting.\nComment 8. 4. Analytical Approach: The authors developed and compared multiple machine\nlearning architectures, including a custom multilayer SVM, AutoGluon ensemble models (e.g.,\nXGBoost), and TabPFN, each trained using distinct feature selection strategies tailored to the\nspectral characteristics of the NMR data. For example, SelectKBest was applied to NMR-M\nfeatures, and recursive feature elimination (RFE) was applied to NMR-M+L (pp. 9–10;\nMethods, lines 461–469). A reduced subset of features (215 and 255, respectively) was used to\naccommodate the dimensional constraints of TabPFN (line 478). Hyperparameter tuning was\nperformed on the development set for the SVM and AutoGluon models, whereas TabPFN\nrequired no tuning, relying instead on its in-context learning framework, which was trained on\nsynthetic priors (line 297- 299). This TabPFN approach represents a novel transformer-based\nsolution capable of producing accurate predictions from small datasets (an essential strength in\nthe context of rare cancers like PDAC). The authors evaluated model performance using\nstandard classification metrics (AUC, sensitivity, specificity, accuracy) and confidence\nintervals derived from bootstrap resampling (Table 2). However, the manuscript would benefit\nfrom the inclusion of additional metrics such as F1 scores, precision-recall curves, or\ncalibration plots, which are particularly valuable in diagnostic applications with class\nimbalance or varying prevalence.\nResponse:\nThanks for the suggestion. F1 scores fit best into our tabular presentation of the models.\nWe incorporated the F1 score into our Table 2 for the evaluation of model performances.\nAdditionally, the PR and Calibration curves are presented below.\na. PR and Calibration curve for three models on the blind test dataset.\nCorresponding changes in the manuscript:\n● Table 2: Include F1 score in the model performance matrices.\nTable 2 Performance and confidence intervals of three models on the blind test dataset.\nComment 9. Suggested Improvements\nExpand discussion on (i) generalizability across ethnicities and different patient cohorts. (ii)\nLimitations regarding metabolite identification from NMR alone (e.g., peak overlap). (iii)\nClarify exclusion criteria for clinical features. Consider including or referencing external\nvalidation efforts (if any are planned) or providing discussion on feasibility. (iv) Include full\nSHAP feature names in visualizations. (v) Explicitly state which SHAP or statistical features\nwere most relevant to early vs. late-stage detection (currently inferred but not quantitatively\nranked).\nResponse:\nWe appreciate the reviewer’s insightful suggestions. We have revised the manuscript to address\nboth points as follows:\n(i) Generalizability across ethnicities and patient cohorts:\nTo evaluate the generalizability of PanMETAI beyond the original cohorts, we have included\nan additional, geographically and dietarily distinct European cohort from the Lithuanian\nUniversity of Health Sciences (Revised Table 1). This external validation demonstrated\nexcellent performance, given the different characteristics of the two populations, with an AUC\nof 0.93, 90% sensitivity, and 83% specificity for pancreatic cancer detection (New Table 3).\nThese findings support the robustness and transferability of our model across diverse\npopulations. The relevant results and interpretation have been added to the revised manuscript.\nCorresponding changes in the manuscript:\n● Page 3, Abstract section, Lines 83-87: We added the description of the European\nvalidation cohort performance.\n● Page 7, Results section, Lines 227-234: We added the description of the European\nvalidation cohort and the model performance of PanMETAI.\n● Page 10, Discussion, Lines 358-361: Discussion of the generalizability of PanMETAI\nmodel.\n● Revised Table 1: Characterization for PDAC and HRC in the 3rd external validation\ncohort from Lithuania.\n● New Table 3: List the performance metrics (AUC, sensitivity, specificity, F1 score) of\nthe PanMETAI model evaluated in the 3rd external Lithuania cohort.\nTable 1. Characteristic clinical demographic data for pancreatic ductal adenocarcinoma (PDAC)\npatients and high-risk controls (HRC) in the 1st exploration, 2nd validation, and 3rd external\nvalidation cohorts.\nTable 3. Diagnostic performance of PanMETAI in the external Lithuanian cohort (N=322).\n(ii) Limitations regarding metabolite identification from NMR alone:\nWe also recognize the inherent limitations of 1H NMR in resolving overlapping metabolite\nsignals. While the use of standardized acquisition protocols (e.g., Bruker IVDr platform), peak\noverlaps in crowded spectral regions (e.g., 3.2-4.0 ppm) may still affect the confidence of\nindividual metabolite identification. We have added a statement in the Discussion section to\nacknowledge this limitation and suggest complementary HRMS-based analysis in future\nstudies to improve metabolite annotation where needed.\nCorresponding changes in the manuscript:\n● Page 12, Discussion section, Lines 418-425: Limitations regarding metabolite\nidentification from NMR\n(iii) Exclusion criteria for clinical features:\nThe rationale for focusing on age and CA19-9 as the clinical variables included in the final\nmodel is supported by clustering analysis (using Silhouette scores and UMAP), which\nindicates that they provide the highest discriminatory power.\nFor features like HbA1c, BMI, and fasting glucose, although they are shown to have significant\ndifferences between groups, their missing data in the clinical record making them not ideal in\nour model development, as they are also not standard PDAC monitoring. TabPFN itself can\nhandle missing data, but most of other methods cannot, which lead to an unfair baseline state\nif trying to compare performances between TabPFN and others.\n(iv) Include full SHAP feature names in visualizations:\nWe include the full SHAP-identified feature names in the new Supplemental Table 3.\nAdditionally, we performed a deeper biological pathway analysis or metabolic network\ninterpretation in the new Figure 5, based on Reviewer 1’s suggestion. By mapping the\nidentified features to disrupted biochemical pathways could significantly enhance the model\ninterpretation and translational relevance of the findings.\nCorresponding changes in the manuscript:\n● New Supplemental Table 3: Annotated SHAP plot with corresponding metabolites and\nfeature trends.\n● Move original Supplemental Table 3 to Supplemental Table 4.\n● New Figure 5: Metabolic pathway, network, and disease and function analyses\nvisualizations for SHAP-identified metabolic features using MetaboAnalyst 6.0 and IPA\nsoftware.\n● Move original Figure 5 to Figure 6\nSupplemental Table 3. SHAP Feature Annotation with Metabolite Assignments and Stage-\nDependent Trends in Pancreatic Cancer.\nFigure 5. Pathway and network analysis of SHAP-identified serum metabolites in PDAC.\n(A) Metabolic Pathway Enrichment Map: Pathway analysis performed using MetaboAnalyst 6.0. The\ngraph shows metabolic pathways significantly enriched by SHAP-ranked metabolites (e.g., 3-\nAminoisobutanoic acid, Glucose, Taurine, TMAO). The y-axis represents statistical significance (-\nlog (p-value)), while the x-axis indicates pathway impact (based on topological analysis, reflected by\n10\nbubble size). Bubble color signifies statistical significance, ranging from red (highly significant) to\nwhite (non-significant). (B) Metabolic Interaction Network: A network generated by Ingenuity Pathway\nAnalysis (IPA) illustrating interactions within Carbohydrate Metabolism, Molecular Transport, and\nSmall Molecule Biochemistry. Red-colored nodes represent molecules found to be increased in PDAC\npatients (lighter colors indicate a less elevated trend), while white nodes have no directional prediction.\nSolid lines represent direct interactions, and dashed lines represent inferred indirect interactions. Arrows\nindicate the directionality of molecular influence or regulation. (C) Disease and Biofunctions Summary:\nThe top 20 disease and functional categories identified by IPA based on the SHAP-identified\nmetabolites, presented by the -log (p-value) of enrichment. This validates the biological relevance of\n10\nthe selected features by linking them to hallmark themes in cancer progression, organismal injury, and\nlipid metabolism.\n(v) Explicitly state which SHAP or statistical features were most relevant to early vs.\nlate-stage detection:\nSHAP value itself is a quantitative measurement – here we subtract the SHAP means between\nearly and late detection and select the features with most positive and the most negative changes.\nThat is to say, these two features play the “most different role” between early-stage detection\nand late-stage detection.\na. Difference of SHAP mean between early- and late-stage detection.\nFeature mean of SHAP values on mean of SHAP values on late- Difference from\nearly-stage detection stage detection early to late\nCA19-9 0.41 0.49 + 0.08\nNMR-M+L 0.03 0.02 - 0.01\n3.7392 ppm\n(Glycogen,\nGlucose)\nAs shown in Table a., a positive difference in CA19-9 indicates that it helps in detection more\nin the late stages. In contrast, a negative difference in signal on NMR-M+L at 3.7392 ppm\nsuggests its contribution to cancer detection decreases in the late stages.\nComment 10. 5. Clarity and Context: The manuscript is clearly written, accessible to readers\nacross disciplines, and well contextualized with current literature. The introduction and\ndiscussion provide a balanced overview of the PDAC diagnostic landscape, highlighting gaps\nand justifying the proposed approach. The figures are informative, although Figure 5 would\nbenefit from enhanced visual clarity and explanation. Importantly, the figure captions are not\nclearly presented in the main body or as a standalone figure legend section, making it difficult\nto interpret the figures independently of the text. For a manuscript of this complexity, full, self-\ncontained figure legends are essential to ensure clarity.\nResponse 10:\nWe thank the reviewer for the constructive feedback. To improve clarity, we have revised the\nFigure to enhance both its visual clarity and explanation. The original Figure 5 has been\nrelocated to Figure 6, as we now include a new Figure 5 that displays the pathway and network\nanalysis of SHAP-identified serum metabolites in PDAC.\nChanges in manuscript:\n● Page 23-24, Figure Legends section (Figure 6), Lines 837-845: Revised figure caption\nand include explicit explanations in the figure legend to improve clarity.\nFigure 6. PanMETAI achieves high performance with a limited training sample size.\nBox plots illustrate the distribution of model accuracy across 20 independent training runs, using\nrandomly selected patient subsets of varying sizes (n = 2 to 199). For each subset size, PanMETAI was\ntrained and assessed for performance stability and generalizability. As shown, the model achieves\nconsistent and high accuracy (≈0.90) with as few as 50–70 training samples. The boxes represent the\ninterquartile range (IQR), the horizontal line within each box indicates the median, and individual points\ndenote outliers. The corresponding AUC, sensitivity, and specificity are reported in Supplemental\nFigure 8.\nComment 11. 6. References: The manuscript cites relevant and recent work, including\nfoundational studies in metabolomics, AI-based diagnostics, and PDAC biology. The\nreferences are comprehensive and appropriate.\nResponse:\nWe appreciate the reviewer’s positive evaluation regarding the quality and relevance of the\ncited references. In response to the review process, we have updated the manuscript to include\nadditional recent studies related to metabolism rewiring in PDAC, aligning with our findings\non the annotation of SHAP-identified metabolic features and the strengths and limitations of\nthe TabPFN-based model, thereby further strengthening the contextual foundation of our work.\nChanges in manuscript:\n● Page 20, References section, Lines 704-708: Include the PDAC metabolic rewiring\nreferences, as shown below ref. 33 & 34.\n● Page 21, References section, Lines 737-741: Include the references related to the\nTabPFN-based model strength and limitations, as shown below ref. 47 & 48.\nRef:\n33. Encarnación-Rosado, J. & Kimmelman, A.C. Harnessing metabolic dependencies in pancreatic\ncancers. Nature Reviews Gastroenterology & Hepatology 18, 482-492 (2021).\n34. Halbrook, C.J. & Lyssiotis, C.A. Employing Metabolism to Improve the Diagnosis and\nTreatment of Pancreatic Cancer. Cancer Cell 31, 5-19 (2017).\n47. Helli, K., Schnurr, D., Hollmann, N., Müller, S. & Hutter, F. Drift-Resilient TabPFN: In-\nContext Learning Temporal Distribution Shifts on Tabular Data. arXiv:2411.10634 (2024).\n48. Ye, H.-J., Liu, S.-Y. & Chao, W.-L. A Closer Look at TabPFN v2: Understanding Its Strengths\nand Extending Its Capabilities. arXiv:2502.17361 (2025).\nComment 12. 7. Expertise Scope: The evaluation was performed from the perspective of\nexpertise in sample preparation, clinical metabolomics, diagnostic model development, and\ntranslational oncology. While the reviewer did not independently verify the TabPFN\nimplementation or code, the methods appear to be appropriately adapted from the cited sources\nand aligned with current best practices in AI model development. However, based on\nestablished protocols in serum metabolomics, a major methodological concern arises from the\nsample preparation workflow, which does not include any step for protein removal (e.g.,\nultrafiltration or precipitation). This omission is likely to result in compromised spectral quality\ndue to macromolecular interference, raising questions about the reliability of downstream\nmetabolite quantification and model performance. This issue must be addressed for the study’s\nconclusions to be fully validated.\nResponse 12:\nWe thank the reviewer for their rigorous review, particularly their acknowledgment of the\nstudy's methodological soundness in AI analysis, the strength of using a blinded validation\ncohort, and the appropriate adaptation of methods from cited sources. We also appreciate the\nreviewer's identification of the specific concern regarding our sample preparation protocol.\nWe would like to clarify that our minimal sample preparation approach was intentional and\naligns with standardized protocols designed for high-throughput clinical applications, although\nwe acknowledge the theoretical risks of macromolecular interference.\na. Alignment with Clinical Standards (IVD-Grade Protocol): Our protocol follows the\nstandard established by the Bruker IVDr (In Vitro Diagnostic Research) platform. This\nminimal preparation method is a deliberate choice in large-scale clinical metabolomics,\ndesigned to maximize standardization and reproducibility—crucial factors for clinical\ndeployment—while retaining the maximum biological information, including signals from\nlipoproteins and associated small molecules.\nb. Technical Mitigation of Interference: We successfully mitigated the impact of\nmacromolecular interference (e.g., from albumin) through the use of specific NMR sequences:\nThe CPMG (Carr-Purcell-Meiboom-Gill) pulse sequence was employed explicitly for the\nNMR-M spectrum to effectively suppress broad macromolecular signals and isolate small,\nmobile metabolites.\nThe overall data quality was confirmed by passing the IVDr platform's stringent Quality\nControl (QC) criteria, which validate essential parameters such as metabolite linewidths and\nsignal-to-noise ratios.\nThe fact that our model, trained on data prepared using this method, achieved outstanding\nperformance (AUC 0.99) on an independent external validation cohort strongly suggests that\nthe methodological choice did not undermine the reliability of the downstream\nclassification performance.\nWe believe this approach enhances the translational viability of PanMETAI, as it relies on a\nrapid, standardized protocol compatible with clinical laboratory workflows. We will clarify\nthis rationale in the revised manuscript to ensure the conclusion is fully validated in the context\nof our translational aims.\nReviewer #4 (Remarks to the Author):\nComment 1. PanMETAI: A High-Performance Tabular Foundation Model for Accurate\nPancreatic Cancer Diagnosis via NMR Metabolomics Wu et al, presenting a very nicely written\nmanuscript, of a case control study (Pancreatic cancer vs healthy controls). The aim of the work\nis to discover a novel pancreatic cancer biomarkers, based on NMR metabolomic profile and\nMachine learning. Case-control: 902serum samples, 478 cancer patients before treatment. 424\nhigh risk patient group. The novelty of the work sits within interpretation of complex NMR\nspectra utilising various deep learning methods, rather than targeted biomarker quantification\nanalysis. Introduction: CA19.9 is not a standard biomarker for diagnosis, and is not\nrecommended for screening by any guidelines.\nResponse:\nWe appreciated the reviewer’s summary and recognition. In response to the fact that CA19-9\nis not a standard biomarker for diagnosis, we have revised the description in the manuscript.\nChanges made in the manuscript:\n● Page 4, Introduction section, Lines 115: CA19-9 description has been updated with\nthe removal of the term “standard” to ensure accuracy and avoid unnecessary\nqualification.\nIntroduction\nThe standard serological biomarker, carbohydrate antigen 19-9 (CA19-9)6,7, exhibits limited utility due\nto its elevated levels in various cancers and nonmalignant conditions8,9, rendering it insufficient as a\nstandalone diagnostic marker.\nComment 2. (i) Metabolite are not stable over long period of time, how to be assured that the\nstorage of these samples were not affected over 20 years storage? (ii) How many of the PDAC\npatients had Jaundice at time of sampling, as this is known to cause significant metabolite\ndegradation. How many PDAC patients were respectable at time of sampling, CA19.9 is known\nto be elevated in jaundice patients, therefore it’s inclusion in the model, although make statistical number\nappears better, but this is unlikely to be represented in the non jaundice early cancer patients. Suggest do a separate\nanalysis to non jaundice patients.\nResponse:\nWe thank the reviewer for raising these critical concerns regarding potential temporal drift and\nclinical confounders, which are highly relevant in retrospective studies. We have conducted\nextensive sensitivity analyses to address both points.\n(i) Metabolite Stability Over Long-Term Storage\nWe acknowledge the valid concern regarding metabolite stability over the 10- to 20-year\nsample collection period. While our samples were collected retrospectively, the High-Risk\nControl (HRC) and PDAC groups were recruited in parallel over similar time frames, which\nhelps mitigate the risk of cohort drift.\nTo empirically test the temporal stability of the crucial features identified by PanMETAI, we\nperformed a dedicated analysis:\na. Temporal Stability Analysis: We conducted a 2D-PCA (Principal Component Analysis)\non the data from Stage III/IV PDAC patients, whose samples spanned from 2005 to 2022 (New\nSupplemental Figure 8). We observed no significant temporal shift in the metabolic data.\nb. Performance Stability: Furthermore, we tested the model's sensitivity (accuracy) across\ndifferent years of collection among these advanced PDAC patients, and found no impact on\nPanMETAI's performance.\nThese findings strongly suggest that the metabolic features employed by our model are\ntemporally stable and that long-term storage did not introduce confounding bias into the\nclassification.\nChanges made in manuscript:\n● Page 11, Discussion section, Lines 388-393: Discuss sampling timeframe and\nevaluating strategy of biomarkers drift temporally.\n● New Supplemental Figure 9: (a) 2D-PCA plot on diagnostic features employed by\nPanMETAI, grouped by sample collection years between the beginning (2005-2007)\nand ending (2020-2022). (b) Model performance across PDAC patients from 2005 to\n2022.\nSupplemental Figure 9. Temporal stability of metabolic features selected by PanMETAI model.\n(a) 2D-PCA plot on diagnostic features employed by PanMETAI, grouped by sample collection years\nbetween the beginning (2005-2007) and ending (2020-2022). (b) Model performance across PDAC\npatients from 2005 to 2022.\n(ii) Confounding Effect of Jaundice\nWe appreciate reviewer’s insightful comments regarding the potential confounding effects of\njaundice on metabolite stability and CA19-9 levels. To address this concern, we regrouped the\nPDAC patients in cohort 2 by excluding those who were jaundiced at the time of blood\nsampling (patient characteristics are provided in the New Supplemental Table 8) and\nreanalyzed the data to specifically assess model performance in a jaundice-free subset.\nInterestingly, after excluding jaundiced patients from the blind test set, we observed a trend:\nour model demonstrated a better diagnostic performance in jaundice patients, particularly in\nearly-stage PDAC instead of late-stage patients (New Supplemental Table 9). This\nobservation aligns with the distinct elevation of CA19-9 levels in jaundiced early-stage patients,\nwhereas CA19-9 levels were similar between jaundiced and non-jaundiced groups in late-stage\nPDAC. These results suggest that jaundice majority affects CA19-9 levels and thereby\ninfluence model sensitivity, especially at early stages. However, since the performance remains\nacceptable and it is unavoidable to include PDAC patients with jaundice in real-world settings,\nfurther blind validation in larger cohorts, particularly with more early-stage patients, will be\nimportant to confirm these findings.\nWe have included the following new results in the revised manuscript to reflect this updated\nanalysis. These findings support the robustness of our model in clinically relevant subsets and\nhelp to mitigate concerns regarding potential bias introduced by jaundice-related metabolite\ndegradation or CA19-9 elevation.\nChanges made in the manuscript:\n● Page 11, Discussion section, Lines 384-388: Added a new paragraph discussing the\nexclusion of jaundiced patients and its impact on model performance.\n● New Supplemental Table 8: Characteristics of study participants by PDAC status in a\n2nd validation cohort excluded for jaundiced patients.\n● New Supplemental Table 9: Analysis of Blind Test PDAC patients with or without\njaundice\nSupplemental Table 8. Characteristic of study participants by PDAC status in a 2nd validation\ncohort excluded for jaundiced patients (N =437)\nSupplemental Table 9. Analysis on Blind Test PDAC patients with or without jaundice\nComment 3. The discovery and validation of the test done on internal re-validation. It would\nbe preferable to have external re-validation on independent samples. However, I appreciate that\nmight be difficult to achieve because of lack of these samples\nResponse:\nWe appreciate the reviewer’s comment regarding the importance of external validation. To\naddress this, we actively collaborated with Professor Juozas Kupcinskas, MD, PhD, Head of\nthe Gastroenterology Department and Institute for Digestive Research at the Lithuanian\nUniversity of Health Sciences, to obtain an independent cohort of PDAC serum samples. The\nexternal validation (N=322) using this geographically distinct European cohort demonstrated\na strong model performance, with an AUC of 0.93, sensitivity of 90%, and specificity of 83%\nin detecting PDAC patients from high-risk populations. This external re-validation confirms\nthe robustness and generalizability of the PanMETAI model beyond the original study\npopulation.\nCorresponding changes in the revised manuscript:\n● Page 3, Abstract section, Lines 83-87: We added the description of the European\nvalidation cohort performance.\n● Page 7, Results section, Lines 227-234: We added the description of the European\nvalidation cohort and the model performance of PanMETAI.\n● Page 10, Discussion, Lines 358-361: Discussion of the generalizability of the PanMETAI\nmodel.\n● Add clinical demographic data of the 3rd external validation cohort in the revised Table\n1. Add New Table 3: List the performance metrics (AUC, sensitivity, specificity, F1\nscore) of the PanMETAI model evaluated in the 3rd external Lithuania cohort.\nTable 1. Characteristic clinical demographic data for pancreatic ductal adenocarcinoma (PDAC)\npatients and high-risk controls (HRC) in the 3rd external validation cohorts.\nTable 3. Diagnostic performance of PanMETAI in the external Lithuanian’s cohort (N=322).\nComment 4. PPV and NPV dependent on disease prevalence, therefore, can not be accurately\ncalculated in case control studies. I suggest this to be removed. and focus on AUC, Sensitivity\nand specificity.\nResponse:\nWe acknowledge and agree with the reviewer's comment.\nWe recognize that Positive Predictive Value (PPV) and Negative Predictive Value (NPV)\nare inherently dependent on the prevalence of the disease in the sampled population, which is\nsignificantly different in a case-control study compared to the real-world population.\nCalculating and reporting PPV and NPV in this setting can be misleading.\nTherefore, we accept the suggestion to remove PPV and NPV from our reported metrics. We\nwill focus our performance evaluation exclusively on metrics that are independent of\nprevalence, namely the AUC, Sensitivity, Specificity, and the F1 score (as suggested by\nanother reviewer).\nCorresponding changes in the revised manuscript:\n● Page 7, Results section, Lines 223-225: Delete the description of NPV and PPV result.\n● Page 22, Figures and Figure Legends section (Figure 2), Lines 786-787: Remove\nNPV and PPV in the Figure and Figure legend.\nFigure 2 Diagnostic performance of the three outperformed models on the blind test dataset.\n(A) Confusion matrices for the three outperformed models evaluated on the independent blind test\ndataset. Pos., positive; Neg., negative; Sen., sensitivity; Spe., specificity. (B) Predictive accuracy\ncomparison of the three outperformed models on the blind test dataset. The class ratio (cancer and\nnoncancer groups) within the blind test dataset is balanced, the performance metric was presented as\naccuracy. Error bars represent one standard deviation of the mean accuracy derived from 1000 bootstrap\nresampling.\nComment 5. limitations in large-scale validation and clinical superiority over CA19-9 persist.\nThe limitation is mostly related to lack of studies testing these biomarkers in blood samples\nfrom early stages PDAC and at non symptomatic stages.\nAs these are cases of established cancer, CA19.9 likely not to be as effective in algorithms in\nearly stages and non symptomatic individuals as been seen previously. Although some reports\nsuggest mild increase in CA19.9 might be an early change as early as 2 years prior to\nsymptomatic diagnosis, but this has not been proven in repeated reports.\nThe authors need to shorten the discussion, and put a paragraph focusing on limitations of their\napproach and how easy to reproduce this analysis in a different lab setting,\nfurthermore, the strength of the study needs to be highlighted. I will be happy to review a revised manuscript if\nneed to.\nResponse:\nWe appreciate the reviewer’s insightful comment regarding the limitations of CA9-9,\nespecially in early-stage and asymptomatic PDAC. Indeed, CA19-9 is not an ideal standalone\nbiomarker for early detection of PDAC, where its sensitivity is limited. However, in our clinical\nfeature selection analysis, CA19-9 demonstrated a high silhouette score, indicating strong\ndiscriminatory power in combination with age among the clinical variables assessed (see\nSupplemental Figure 4). Therefore, we included CA19-9 as one of the diagnostic components\nin our PanMETAI model. Moreover, our ablation analysis (see Supplemental Table 2)\nrevealed that combining CA19-9 with metabolic features and Activin A protein levels\nsignificantly improved the overall diagnostic performance of the model compared to a single\nmetabolic modality alone. This finding supports the rationale for incorporating CA19-9 into an\nintegrated diagnostic strategy, rather than relying on it in isolation. Together, these results\nhighlight the added value of a multimodal biomarker approach in improving diagnostic\naccuracy for PDAC, particularly in high-risk populations.\nAdditionally, we condensed the discussion section, which was repetitive, and included the\nstrengths/ limitations of this study to further enhance the comprehensive understanding of\npancreatic cancer research.\n(i) NMR-based metabolomics approach:\nStrengths. NMR-based approach adheres to well-established and standardized protocol\nrecommended by the Bruker IVDr platform, which is widely adopted in large-scale\nmetabolomics and clinical diagnostic studies. This standardization not only facilitates future\ndata integration and cross-center comparability but also enables the concurrent analysis of lipid\nprofiles, thereby enhancing the robustness and translational potential of our findings.\nLimitations. While SHAP-based annotation revealed distinct metabolic associations between\nearly- and late-stage pancreatic cancer, enabling subsequent pathway and network analyses\n(Figure 5), the underlying biological mechanisms remain speculative and require further\nvalidation. Moreover, inherent technical limitations of 1H NMR spectroscopy, particularly in\nresolving overlapping signals in crowded spectral regions, can constrain the confident\nidentifications of specific lipid species. To address these challenges, future iterations of the\nPanMETAI model will integrate complementary mass spectroscopy-based lipidomic profiling.\nThese additions aim to expand the metabolite coverage and further enhance diagnostic\nperformance, especially for those lipids such as long-chain sphingomyelins, ceramides, and\n(lyso)phosphatidylcholines have been reported as informative biomarkers for pancreatic cancer\n(Ref 1).\n(ii) TabPFN-based PanMETAI model:\nStrengths. One of the main strengths of TabPFN is its ability to perform in-context learning.\nOnce pre-trained, it makes predictions for new datasets in a single forward pass, without\nrequiring additional optimization or hyperparameter tuning. This eliminates the need to\nrepeatedly search for optimal hyperparameters when training data is obtained from different\ninstitutions with slight variations in experimental methods. Additionally, TabPFN is robust to\nmissing values and outliers, and emerging studies suggest it can also handle certain\ndistributional shifts (Ref 2). For classification and regression tasks involving up to 10,000\nsamples and 500 features, TabPFN consistently outperforms tuned gradient-boosted decision\ntrees (e.g., CatBoost, LightGBM), deep tabular models (e.g., FT-Transformer), and AutoML\nsystems such as AutoGluon (Ref 3).\nLimitations. However, it is designed primarily for smaller datasets, and its performance,\nlatency, and memory consumption degrade significantly beyond 10,000 samples or 500\nfeatures. While methods to extend to larger datasets do exist (Ref 4) we haven’t implemented\nthem as we observe sufficient performance. Another limitation is, although TabPFN integrates\nwith SHAP and Leave-One-Covariate-Out (LOCO) for interpretability, the Transformer-based\narchitecture remains less interpretable than traditional models like decision trees or linear\nregression (Ref 5).\nRef:\n1. Wolrab, D., et al. Lipidomic profiling of human serum enables detection of pancreatic cancer.\nNature communications 13, 124 (2022).\n2. Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter, Drift-Resilient\nTabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data, NeurIPS 2024\n(https://openreview.net/forum?id=p3tSEFMwpG)\n3. Han-Jia Ye, Si-Yang Liu, Wei-Lun Chao, A Closer Look at TabPFN v2: Strength, Limitation,\nand Extension, https://arxiv.org/abs/2502.17361v1 (2025)\n4. Junwei Ma, Valentin Thomas, Guangwei Yu, Anthony Caterini, In-context data distillation\nwith TabPFN, ICLR 2024 Workshop on Understanding of Foundation Models (ME-FoMo)\n(https://arxiv.org/abs/2402.06971)\n5. Rundel, D., Kobialka, J., von Crailsheim, C., Feurer, M., Nagler, T., Rügamer, D. (2024).\nInterpretable Machine Learning for TabPFN. In: Longo, L., Lapuschkin, S., Seifert, C. (eds)\nExplainable Artificial Intelligence. xAI 2024. Communications in Computer and Information\nScience, vol 2154. Springer, Cham. https://doi.org/10.1007/978-3-031-63797-1_23\n(https://arxiv.org/abs/2403.10923)\nCorresponding changes in the revised manuscript:\n● Page 9-12, Discussion section: Shorten the Discission section with those redundant\ndescriptions.\n● Page 10 & 11-12, Discussion section, Lines 349-361 & 401-410: Discuss how easy it is\nto reproduce this analysis in a different lab setting, and strength of the study.\n● Page 12, Discussion section, Lines 412-425: Include a paragraph focusing on limitations\nof this approach and future complementary method to include Mass spectroscopy-based\nlipidomics and add a more critical discussion of TabPFN’s strengths and limitations.\nSupplemental Figure 4. Clinical data Feature Selection.\n(a) Clinical Feature Processing Pipeline: This panel illustrates the steps involved in the exclusion and\nselection of clinical features for analysis. (b) Silhouette Score Analysis of Clinical Feature\nCombinations: Silhouette scores were calculated to evaluate the quality of UMAP clusters formed using\ndifferent subsets of clinical features. To ensure the robustness and reproducibility of the UMAP\nembeddings, each feature combination was tested ten times. The error bars displayed represent one\nstandard deviation of the Silhouette scores across these ten repetitions.\nSupplemental Table 2. TabPFN Blind Test Performance on early and late stage PDAC.\nFinal revisions for Nature Communications manuscript NCOMMS-\n25-34492A-Z\nREVIEWERS' COMMENTS\nDetailed Response to Reviewers\nReviewer #1 (Remarks to the Author):\nAll reviewer comments have been adequately addressed by the authors.\nResponse:\nWe sincerely thank the reviewer for the positive evaluation and supportive\ncomments.\nReviewer #1 (Remarks on code availability):\nI would like to note that the GitHub repository link provided by the authors is not\naccessible. Kindly request the authors to verify the URL or provide an\nalternative public link to allow proper access and validation of the materials.\nResponse:\nWe appreciate the reviewer bringing this to our attention. The GitHub repository\nlink has now been updated to a publicly accessible version. The revised link is\nprovided in the Code Availability section of the manuscript and has been\nverified for full access (https://github.com/PanMETAI/AutoGluon_PDAC).\nReviewer #2 (Remarks to the Author):\nThe authors revisions including the addition of new data and text in response\nto reviewer questions that have significantly improved the paper.\nResponse:\nWe thank the reviewer for the positive assessment.\nComment 1:\nSome additional comments;\nThe authors now provide additional data regarding age by performing a\nsubgroup analysis comparing age, bmi and smoking matched cases and\ncontrols, showing that the model much of its performance. So, its not clear why\nage needs to be added to the final model.\nResponse:\nThank you for highlighting this important point. To clarify this and address\npotential confounding effects, we previously performed an additional subgroup\nanalysis using Cohort 2, where age, BMI, and smoking were matched\n(Supplemental Tables 5). As shown in R. Table 1, PanMETAI maintained\nstrong overall performance (AUC 0.97), demonstrating that the metabolic\nfeatures remain the primary predictors. The specificity showed a modest\ndecrease from 0.94 to 0.89, reflecting the contribution from age in distinguishing\nhigh-risk controls from PDAC — a pattern consistent with established\nepidemiological risk profiles. Furthermore, silhouette ranking analysis\n(Supplemental Figure 4) demonstrates that age provides a modest but non-\nnegligible discriminative value when integrated with other variables such as\nCA19-9 than CA19-9 alone. Hence age is retained in the final model to ensure\nmaximal completeness and stability.\nR. Table 1. TabPFN’s performance on the original and sub-cohort with no\nsignificant age, BMI, and smoking differences between cases and controls.\nModel Accuracy Sensitivity Specificity AUROC\nTabPFN 0.94 0.93 0.94 0.99\n(N=529) (0.91-0.95) (0.89-0.96) (0.91-0.97) (0.98-0.99)\nTabPFN 0.91 0.92 0.89 0.97\n(N=391; w/o age, BMI, (0.88-0.94) (0.89-0.96) (0.84-0.94) (0.96-0.99)\nsmoke differences)\nComment 2:\nThe authors include activin A and compare levels between cases and controls,\nbut they do not provide any AUC or sensitivity data for including it. The\ndifferences in cases and controls are relatively modest. Does overall model\nperformance change much at all by leaving it out?\nResponse:\nWe appreciate the reviewer’s insightful question regarding the specific\ncontribution of Activin A to the model's performance. To address this, we\nperformed an ablation analysis comparing PanMETAI performance with and\nwithout Activin A in both the Taiwanese and Lithuanian cohorts (R. Tables 2\nand 3).\nWhile the inclusion of Activin A yielded only marginal improvements in the\nTaiwanese cohort (AUC 0.986 vs. 0.983), it proved critical for maintaining\nmodel robustness in the external Lithuanian cohort. Specifically, removing\nActivin A from the Lithuanian dataset decreased overall AUC (from 0.926 to\n0.912) and specificity (from 83.4% to 80.0%). Most importantly, the inclusion of\nActivin A significantly improved sensitivity for Stage I PDAC (increasing from\n63.6% to 72.7%).\nThese findings suggest that while other markers may drive most of the\npredictive power in the discovery cohort, Activin A is essential for mitigating\npopulation-specific heterogeneity and ensuring high sensitivity for early-stage\ndisease across diverse populations. Therefore, we have retained it to provide\nthe model's generalizability and clinical utility.\nR. Table 2. Ablation analysis of Activin A contribution to model\nperformance in the Taiwanese blind-test PDAC cohort.\nModel Group N Sensitivity Specificity AUC\nOverall 529 0.927007 0.945098 0.985702\nHRC 255 NaN 0.945098 NaN\nTabPFN- PDAC1 12 0.750000 NaN NaN\nPanMETAI\nPDAC2 25 0.800000 NaN NaN\n(w/ Activin A)\nPDAC3 46 0.891304 NaN NaN\nPDAC4 191 0.963351 NaN NaN\nOverall 529 0.916058 0.952941 0.983255\nHRC 255 NaN 0.952941 NaN\nTabPFN- PDAC1 12 0.750000 NaN NaN\nPanMETAI\nPDAC2 25 0.840000 NaN NaN\n(w/o Activin A)\nPDAC3 46 0.913043 NaN NaN\nPDAC4 191 0.937173 NaN NaN\n*PDAC1-4 denotes pancreatic ductal adenocarcinoma stages I to IV.\nIn the Taiwanese cohort, incorporating Activin A yielded only marginal\nimprovements in AUC compared with the model without Activin A.\nR. Table 3. Ablation analysis of Activin A contribution to model\nperformance in the Lithuanian external blind-test PDAC cohort.\nModel Group N Sensitivity Specificity AUC\nTabPFN- Overall 314 0.893491 0.834483 0.925770\nPanMETAI HRC 145 NaN 0.834483 NaN\n(w/ Activin A)\nPDAC1 11 0.727273 NaN NaN\nPDAC2 50 0.860000 NaN NaN\nPDAC3 32 0.906250 NaN NaN\nPDAC4 76 0.934211 NaN NaN\nOverall 314 0.881657 0.800000 0.912304\nHRC 145 NaN 0.800000 NaN\nTabPFN- PDAC1 11 0.636364 NaN NaN\nPanMETAI\nPDAC2 50 0.840000 NaN NaN\n(w/o Activin A)\nPDAC3 32 0.906250 NaN NaN\nPDAC4 76 0.934211 NaN NaN\n*PDAC1-4 denotes pancreatic ductal adenocarcinoma stages I to IV.\nIn the Lithuanian cohort, incorporating Activin A showed a visible improvement\nin AUC compared with the model without Activin A.\n(For the records, the original TabPFN version previously used has been\nremoved from its repository and replaced with the newly released TabPFN\nv2.0.9 on GitHub. We verified that models outputs generated using TabPFN\nv2.0.9 are indistinguishable from the prior version, with performance metrics\nmatching to the third decimal point after the comma. Accordingly, the ablation\nanalysis presented here is performed using TabPFN v2.0.9, which is currently\nopenly accessible online.)\nComment 3:\nThe final model includes age, activin and CA19-9 along with the MR spec data.\nIt is likely that most of the diagnostic power comes from the CA19-9 and the\nMR spec data, without the need to include age and activin.\nResponse:\nThank you for reviewer’s comment. As above demonstration, our ablation study\nshows that the combination of all four components—metabolomics, age, CA19-\n9, Activin A—consistently improves performance, supporting a multimodal\nrather than unimodal diagnostic strategy.\nComment 4:\nThe authors’ use of the term clinical factors is unhelpful as CA19-9 is not a\nclinical factor and age should simply be described as age. Clinical factors\nsuggest symptoms or clinical signs are being incorporated into their model.\nResponse:\nThank you for the reviewer’s suggestion. We have revised terminology\nthroughout the manuscript:\n“Clinical factors” → “Clinical/Biochemical parameters”\nAll changes are implemented in the manuscript, tables, and figures.\nComment 5:\nThe authors perform a subset analysis excluding jaundiced cases and it\nappears that they are describing the model performance, but its not clear in the\ntext or in Suppl Table 9 whether the sensitivity data they present represents the\nPanMETAI model result or the CA19-9 sensitivity.\nResponse:\nWe thank the reviewer for bringing this to our attention.\nWe have clarified in the title of Supplemental Table 9 that the sensitivity refers\nto the full PanMETAI model (NMR + Activin A + CA19-9 + age). We have also\ncorrected the patient numbers reported in Table S9, as the previous version\ncontained a typo error.\nSupplemental Table 9. Diagnostic performance analysis on blind test\nPDAC patients with or without jaundice in the PanMETAI model\nComment 6:\nHow does the MR model (NMR-M-L) perform on its own in patients without\njaundice?\nResponse:\nWe thank the reviewer for this important question.\nTo address it, we performed an additional evaluation using NMR-only features\n(NMR-M/NMR-M+L spectra) in the non-jaundiced subgroups, and compared\nthese results with the full TabPFN-PanMETAI model, which includes all\nvariables. As shown in R. Table 4, removing clinical and protein biomarkers\nsubstantially reduced discriminatory ability—AUC decreased from 0.980 to\n0.888—indicating that the NMR-only model is insufficient its own in non-\njaundiced subgroups. This finding confirms that the integration of clinical and\nprotein biomarker meaningfully enhances predictive accuracy.\nR. Table 4. Diagnostic performance of TabPFN model in non-jaundiced\npatients from the Taiwanese blind-test cohort.\nModel Group N Sensitivity Specificity AUC\nOverall 418 0.791411 0.847059 0.888055\nHRC 255 NaN 0.847059 NaN\nNMR only\nPDAC1/2 19 0.631579 NaN NaN\nPDAC3/4 144 0.812500 NaN NaN\nOverall 418 0.889571 0.945098 0.980464\nTabPFN-\nHRC 255 NaN 0.945098 NaN\nPanMETAI\n(NMR + CA19-9 +\nPDAC1/2 19 0.631579 NaN NaN\nage + Activin A)\nPDAC3/4 144 0.923611 NaN NaN\n*PDAC1/2 & 3/4 denotes pancreatic ductal adenocarcinoma stages I/II and\nIII/IV.\nComment 7:\nOther papers that have evaluated MR spec for pancreatic cancer diagnosis\nshould be cited.\nResponse:\nWe thank reviewer’s recommendation. The following references related to\nNMR spectroscopy for pancreatic cancer diagnosis have been cited in the\nDiscussion.\nInclude the original cited reference:\n28. Bae, G., et al. Quantitative Metabolomics and Lipoprotein Analysis of PDAC\nPatients Suggests Serum Marker Categories for Pancreatic Function,\nPancreatectomy, Cancer Metabolism, and Systemic Disturbances. Journal of\nProteome Research 23, 1249-1262 (2024).\nAnd the newly added citations:\n47. Battini, S., et al. Metabolomics approaches in pancreatic adenocarcinoma:\ntumor metabolism profiling predicts clinical outcome of patients. BMC\nMedicine 15, 56 (2017).\n48. OuYang, D., Xu, J., Huang, H. & Chen, Z. Metabolomic Profiling of Serum\nfrom Human Pancreatic Cancer Patients Using 1H NMR Spectroscopy and\nPrincipal Component Analysis. Applied Biochemistry and Biotechnology 165,\n148-154 (2011).\nComment 8:\nThe authors use quite a few non-standard abbreviations that reduce readability.\nResponse:\nWe have removed non-essential abbreviations and ensured first-use definitions\nthroughout the manuscript.\nReviewer #2 (Remarks on code availability):\nnA\nReviewer #3 (Remarks to the Author):\nThe authors have provided thorough and well-structured responses to each of\nthe concerns raised in the initial review. The manuscript has been substantially\nimproved in both scientific rigor and clarity. The revised Discussion now\nincludes a balanced and nuanced evaluation of NMR- versus HRMS-based\nmetabolomics, properly recognizing the strengths and limitations of each\nplatform and outlining a credible plan for future integration of HRMS-based\nlipidomics.\nMy major concern regarding serum sample preparation has been fully clarified.\nThe authors have convincingly justified the use of the Bruker IVDr clinical-grade\nworkflow, provided validation metrics (linewidths <1.5 Hz, S/N > 3), and added\nexplicit statements and references demonstrating its acceptance and\nreproducibility in large-scale clinical metabolomics. The newly added\nmethodological details on spectral acquisition, CPMG parameters, and quality\ncontrol verification adequately resolve the prior uncertainties.\nThe explanation regarding the exclusion of clinical variables with incomplete\ndata (e.g., HbA1c, BMI, glucose) is methodologically sound. The clarification\nthat data completeness was prioritized to ensure fair benchmarking across\ndifferent machine-learning models is well reasoned. The inclusion of F1-scores,\nPR curves, and calibration plots further strengthens the evaluation of classifier\nperformance.\nThe overall manuscript reads clearly and cohesively. The Introduction and\nDiscussion are now more balanced, with the revised text properly positioning\nPanMETAI within the context of translational AI-based metabolomics and\nprecision oncology.\nResponse:\nWe greatly appreciate the reviewer’s recognition and positive feedback, as well\nas thank the reviewer and confirm that complete-case analysis was essential\nfor fair benchmarking of ML models.\nReviewer #3 (Remarks on code availability):\nI did not independently review or run the provided code. However, based on the\nmanuscript description, the implementation appears to follow standard\npractices for AI model development, and the methods are transparently\ndescribed with appropriate citations to the original TabPFN framework. The\nreproducibility of the results seems well supported by the detailed\nmethodological documentation, inclusion of performance metrics, and\navailability of data preprocessing steps. I recommend that, upon publication,\nthe authors ensure that the repository includes a complete README file with\nclear installation and execution instructions to facilitate use by the research\ncommunity.\nResponse:\nWe have updated the repository with a comprehensive README, including\ninstallation steps, environment requirements, and execution examples\n(https://github.com/PanMETAI/AutoGluon_PDAC).\nReviewer #4 (Remarks to the Author):\nI have reviewed the revised manuscript and happy with comments and changes,\nin particular the significant addition of validation work, i congratulate the authors\nfor their work\nResponse:\nWe sincerely thank the reviewer for the encouraging feedback."}]

Decision letter