Predicting PSA50 response to

177

Lu-PSMA therapy using machine learning and
automated total tumor volume

Eduardo Rios-Sanchez, Anne-Laure Giraudet, Alicia Sanchez-Lajusticia,
Valentin Pretet, Emilie Paquet, Thomas Baudier, Jean-Noël Badel, David
Sarrut

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To cite this version:

Eduardo Rios-Sanchez, Anne-Laure Giraudet, Alicia Sanchez-Lajusticia, Valentin Pretet, Emilie Paquet, et al. Predicting PSA50 response to

177

Lu-PSMA therapy using machine learning and automated total tumor volume. EJNMMI Physics, 2025, 12 (1), pp.95. 10.1186/s40658-025-00808-6. hal-05370164

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# Predicting PSA50 response to <sup>177</sup>Lu-PSMA therapy using machine learning and automated total tumor volume

Eduardo Rios-Sanchez<sup>1,2,3*</sup> Anne-Laure Giraudet<sup>2</sup>, Alicia Sanchez-Lajusticia<sup>2</sup>, Valentin Pretet<sup>2</sup>, Emilie Paquet<sup>4</sup>, Thomas Baudier<sup>1,2</sup>, Jean-Noël Badel<sup>1,2</sup> and David Sarrut<sup>1,2</sup>

## Abstract

The <sup>177</sup>Lu-PSMA therapy is an established treatment for metastatic castration-resistant prostate cancer (mCRPC), targeting the prostate-specific membrane antigen (PSMA). Despite well-established correlations between <sup>68</sup>Ga-PSMA PET/CT imaging and outcome, predicting individual patient responses remains a significant challenge. This study introduces an automated method for computing the total tumor volume (TTV) from <sup>68</sup>Ga-PSMA PET/CT imaging and develops predictive models to assess patient biological response via the PSA50 criterion. A retrospective analysis was conducted on a real-world data cohort of 139 mCRPC patients treated in our institution. TTV was automatically extracted from PET/CT images and correlated with treatment response, defined by PSA50 criteria. Machine learning models, including Logistic Regression with L1 (LASSO) and Support Vector Machine (SVM), were developed to predict PSA50 response using imaging and clinical features. The best-performing models achieved F1-scores of 0.68 and 0.67, comparable to existing nomograms. Correlation analysis identified TTV-derived features and time since diagnosis as significant predictors of response. The proposed workflow offers an automated and reproducible approach to predicting treatment response in <sup>177</sup>Lu-PSMA therapy. Limitations remain for lesion segmentation within physiological regions.

**Keywords** <sup>177</sup>Lu-PSMA therapy, Machine learning, <sup>68</sup>Ga-PSMA, PSA50, TTV

## Introduction

<sup>177</sup>Lu-PSMA therapy represents a significant advancement in the treatment of metastatic castration-resistant prostate cancer (mCRPC), a condition with limited therapeutic options and poor prognosis. Prostate-specific membrane antigen (PSMA) is a transmembrane protein abundantly expressed on the surface of prostate cancer cells, making it an ideal target for radionuclide therapy [1–3]. Leveraging the radiolabeled compound <sup>177</sup>Lu-PSMA, this therapy delivers targeted radiation to PSMA-expressing cells, minimizing damage to surrounding healthy tissues. Clinical trials [1–3] and studies [4, 5] have demonstrated the efficacy of <sup>177</sup>Lu-PSMA in reducing tumor burden and

<figure><img src="image_4.png" /></figure>

<footnote>*Correspondence:
Eduardo Rios-Sanchez
eduardo.rios-sanchez@creatis.
insa-lyon.fr
¹CREATIS; CNRS UMR 5220; INSERM
U 1044; Université de Lyon; INSA-
Lyon; Université Lyon 1, Lyon,
France</footnote>

<footnote n="2">²Centre de lutte contre le cancer
Léon Bérard, Lyon, France</footnote>

<footnote n="3">³Siemens Healthcare, Courbevoie,
France</footnote>

<footnote n="4">⁴Centre Hospitalier de Saint Denis,
La Réunion, France</footnote>
extending survival in mCRPC patients, with a generally manageable safety profile. This targeted approach not only offers hope for patients with advanced disease but also lays the groundwork for its potential application in earlier stages of prostate cancer.

In the VISION trial [1], patients with mCRPC were eligible for treatment with ¹⁷⁷Lu-PSMA if they presented with at least one PSMA-avid lesion (regardless of location) where uptake was higher than that of the liver, as determined by PET imaging. Key exclusion criteria included the presence of PSMA-negative metastases, as detected by imaging. The size and type of metastasis influence the exclusion decision; for instance, larger metastases with no PSMA uptake were typically grounds for exclusion. At our center, patient selection initially followed these VISION criteria and later transitioned to a standardized PSMA-PET interpretation framework for clinical practice.

To enhance the accuracy of patient selection, ¹⁸F-FDG PET imaging is often used in conjunction with ⁶⁸Ga-PSMA PET scans [1, 6, 7]. This combined approach helps detect discordant lesions, i.e., metastases that show uptake on FDG PET (FDG-positive) but are negative on PSMA PET (PSMA-negative). These "mismatch" lesions generally indicate more aggressive, dedifferentiated tumor biology. Patients with FDG-positive and PSMA-negative lesions suggest resistance to ¹⁷⁷Lu-PSMA therapy and will therefore not be eligible for this treatment.

Multiple criteria are used to evaluate treatment response in patients undergoing ¹⁷⁷Lu-PSMA therapy. One is the PSMA PET Progression Criteria (PPP), which defines disease progression based on the appearance of new PSMA-avid lesions or an increase in the size of existing lesions on PET scans [8]. Another commonly used set of guidelines is the RECIP [9] (Response Evaluation Criteria in PSMA PET/CT), which categorizes patient response into four distinct statuses: Complete Response (CR), Partial Response (PR), Progressive Disease (PD), and Stable Disease (SD). These criteria allow clinicians to assess the efficacy of the treatment by monitoring both tumor burden and the development of new metastatic lesions. Additionally, the Prostate Cancer Working Group 3 (PCWG3) criteria are used for clinical assessment and trial endpoints in advanced prostate cancer [10]. The PCWG3 framework evaluates various parameters, including PSA levels, imaging, and clinical symptoms, to determine overall disease progression, response to treatment, and survival outcomes.

The relationship between ⁶⁸Ga-PSMA PET/CT imaging and treatment response in patients undergoing ¹⁷⁷Lu-PSMA therapy has been extensively studied, with several studies highlighting its potential predictive value. For instance, Groener et al. [11] demonstrated that ⁶⁸Ga-PSMA-11 PET/CT moderately correlates with lesion response to ¹⁷⁷Lu-PSMA-617, suggesting that lesions with lower uptake on pre-therapeutic scans are more likely to progress but can still respond to therapy in a significant number of cases. This study emphasizes the importance of pre-treatment imaging parameters in predicting treatment response. Similarly, Peters et al. [12] found that while pre-therapeutic ⁶⁸Ga-PSMA PET uptake was comparable across lesions, the SUVmax correlated with the absorbed dose, particularly in lymph node lesions, which showed a decrease in volume post-treatment. These findings suggest that pre-therapeutic ⁶⁸Ga-PSMA PET/CT can serve as a valuable tool in identifying potential responders and optimizing personalized dosimetry strategies. However, as Volter et al. [13] observed, there remains some inconsistency in the literature, with their study showing no significant differences
in dosimetry between responders and non-responders, indicating that additional factors
may influence treatment outcomes.

Lesion segmentation in dosimetry plays a crucial role in accurately assessing treatment response. Peters et al. [12] tackled the challenge of small lesion dosimetry by using a sufficiently large volume of interest (VOI) to mitigate partial volume effects (PVE) and then subtracting background signal using a nearby VOI of the same volume. Groener et al. [11] further refined segmentation by selecting up to 10 lesions per patient based on specific criteria, such as non-physiological uptake and detectability on co-registered CT, with SUVmax, SUVmean, and tumor-to-liver uptake ratio (TLR) calculated for each lesion. The lesions were classified into responsive, stable, or progressive categories based on SUVmax changes. Another study by Schmidkonz et al. [14] utilized manual segmentation to derive PSMA-tumor volume (PSMA-TV) from PET/CT images, setting iso-contours at 45% of the maximum uptake. This method offered insights into the detection and characterization of both PET-positive/CT-negative and PET-positive/CT-positive lesions. The segmentation of lesions has been extended to incorporate machine learning approaches, as highlighted by Xu et al. [15], who employed neural networks for automatic lesion segmentation. However, while these models performed moderately well with large, high-intensity lesions, challenges remain in accurately segmenting small or low-intensity lesions.

These findings underscore the need for reproducible segmentation tools to ensure a
consistent and accurate analysis of lesions, particularly in the context of small or low-
uptake lesions. Here, we introduce an automated and reproducible method for comput-
ing the total tumor volume (TTV) from ⁶⁸Ga-PSMA PET/CT imaging and a predictive
model to assess patient response to therapy through PSA50 criterion.

**Materials and methods**

Patient cohort

Patient characteristics

The study involved a retrospective real-world cohort of 139 male patients with mCRPC who received ¹⁷⁷Lu-PSMA treatment at the Léon-Bérard Cancer Center (Lyon, France) between May 2021 and August 2025. All patients had prior hormone therapy, and 124 (89%) had also received chemotherapy. At treatment initiation, the median age was 74 years (IQR: 68–79), and the median time from initial diagnosis was 7.6 years (IQR: 3.9–13.6). Clinical indicators reflected advanced disease: Karnofsky performance status (KPS), Alkaline phosphatase (ALP), and pretreatment PSA levels were elevated, while hemoglobin levels were generally within normal range. The PSA50 response rate (PSA50-RR) in our cohort was 44.6%, indicating that nearly half of the patients achieved a ≥50% decline in PSA levels following treatment. All patients exhibited varying types and numbers of metastases. Bone metastases were present in 131 patients (94.2%), pelvic lymph node metastases in 51 patients (36.7%), and liver metastases in 11 patients (7.9%). Tumor burden also varied, with 45 patients (32.4%) presenting with ≥20 metastatic lesions. The database summary is shown in Fig. 1, with metrics reported as median and IQR.

Patients were classified as responders according to the PSA50 metric, defined by a
reduction of at least 50% in the PSA nadir (the lowest PSA value reached during treat-
ment) relative to baseline (pre-therapy) PSA levels [1, 2]. Based on this criterion, 44.6%
<table><caption>**Fig. 1** Statistics of the database. Median and inter-quartile range</caption><thead><tr><td>Feature</td><td>Cohort (n=139)</td></tr></thead><tbody><tr><td>TTV's SUVmean</td><td>9.13 (7.55 - 11.96)</td></tr><tr><td>Age (y)</td><td>74.00 (68.00 - 79.00)</td></tr><tr><td>Time since diagnosis (y)</td><td>7.57 (3.87 - 13.57)</td></tr><tr><td>PSA pre-treatment (µg/l)</td><td>55.41 (26.60 - 187.50)</td></tr><tr><td>Hemoglobin (g/dL)</td><td>12.00 (10.70 - 13.45)</td></tr><tr><td>ALP (UI/L)</td><td>135.00 (89.00 - 237.00)</td></tr><tr><td>Karnofsky (%)</td><td>80.00 (70.00 - 90.00)</td></tr><tr><td>PSA50 (%)</td><td>-0.37 (-0.85 - 0.21)</td></tr><tr><td>Bone metastases</td><td>131 (94.2%)</td></tr><tr><td>Pelvic node metastases</td><td>51 (36.7%)</td></tr><tr><td>Liver metastases</td><td>11 (7.9%)</td></tr><tr><td>Tumor burden (≥20 metastases)</td><td>45 (32.4%)</td></tr><tr><td><strong>Responders (PSA50-RR)</strong></td><td><strong>44.60%</strong></td></tr></tbody></table>

of the cohort were classified as responders. The database was composed of real-world data, reflecting the variability and heterogeneity of clinical practice outside the controlled settings of clinical trials.

**Inclusion and exclusion criteria for this study**

Among the patients having received the ¹⁷⁷Lu-PSMA treatment, the exclusion criteria included a baseline PSA level below 1 ng/mL, incomplete or missing whole-body ⁶⁸Ga-PSMA PET/CT (from knees to head), or treatment discontinuation for reasons unrelated to cancer progression (e.g. treatment toxicity or death). Among the 139 patients enrolled in the study, 63 (45%) completed all six treatment cycles. Early discontinuation occurred in 74/139 patients based on medical judgment, primarily due to the absence of additional clinical benefit. Notably, two patients discontinued after four cycles because their PSA levels became undetectable and no radiological evidence of disease was observed, and their remaining treatment cycles were reserved for potential disease progression.

**Imaging and assessment protocol**

All included patients underwent at least one ⁶⁸Ga-PSMA PET/CT scan and one ¹⁸F-FDG PET/CT scan as part of the pre-treatment evaluation. PET/CT images were acquired using the Biograph Vision 450 device (Siemens Healthineers) and the Vereos system (Philips Healthcare) with standardized imaging parameters, more information is available in the supplementary material. A patient will not be considered to therapy if the lesions have an uptake in the ¹⁸F-FDG PET/CT scan and not in the ⁶⁸Ga-PSMA PET/CT scan; i.e. if there is a FDG+/PSMA- mismatch. Still, there is a potential interest in combining ¹⁸F-FDG and ⁶⁸Ga-PSMA imaging to improve predictive power.

The treatment protocol involved administering up to 6 cycles of ¹⁷⁷Lu-PSMA, with each cycle consisting of 7.4 GBq. Two radiopharmaceutical drugs were utilized: ¹⁷⁷Lu-PSMA-1 (33 patients) and ¹⁷⁷Lu-PSMA-617 (104 patients), and 2 patients receiving both drugs. Not all patients completed the full treatment course of 6 cycles: 8 patients (5.8%) received one cycle, 31 (22.3%) received two cycles, 10 (7.2%) received three cycles, 21
(15.1%) received four cycles, and 6 (4.3%) received five cycles, while 63 patients (45.3%) completed the entire treatment regimen.

Monitoring of PSA levels was conducted for all patients, both before and after the treatment, as well as at intervals between each cycle. An example of the PSA levels during the treatment of two patients is shown in Fig. 2: a treatment responder and a non-responder.

## Overview of the proposed workflow

The objective was to establish an automated workflow that processes <sup>68</sup>Ga-PSMA PET/CT images and biological/clinical values as input and generates correlation and treatment response predictions. The workflow begins with obtaining the Total Tumor Volume (TTV), which represents the PET/CT image devoid of physiological uptake and noise, retaining only the tumoral uptake. Once the TTV is determined, image-derived features are extracted, while clinical features are considered from the patient clinical records. In parallel, the biological target, PSA50, is calculated using the PSA nadir and initial PSA levels. These collected features and targets are subsequently utilized to generate correlation analysis and to predict each patient's likelihood of responding to treatment based on PSA50.

## Simple automated TTV segmentation

The goal is to separate the physiological uptake and noise from the lesion uptake in the <sup>68</sup>Ga-PSMA PET images. First, a list of physiological organs exhibiting normal PSMA uptake was defined: gallbladder, stomach, pancreas, small bowel, colon, duodenum, urinary bladder, kidneys, liver and spleen. Those regions were considered to depict high SUV values in the PET image while, in general, not corresponding to lesion uptake. The contours of these regions were automatically extracted from the CT images of each

<figure><img src="image_5.png" /><figcaption>**Fig. 2** Example of PSA levels evolution during <sup>177</sup>Lu-PSMA treatment for two patients. The therapy cycles are marked by green diamonds and <sup>68</sup>Ga-PSMA PET/CT scan dates are indicated by red squares. In this example, the decreasing PSA levels indicate a biological treatment response for patient 1 (blue curve) and the increasing PSA of the yellow curve shows non-respondant patient 2. The dashed lines represent the PSA50% threshold. The dates have been falsified but represent the real interval of the treatment</figcaption></figure>
patient using TotalSegmentator [16] version 2 without any manual correction. To deal with the partial volume effect, the anatomical segmentation of organs in the abdomen – the gallbladder, stomach, pancreas, small bowel, colon, duodenum, and urinary bladder – were dilated by 5 mm. For the kidneys, liver, and spleen, a dilation of 10 mm was applied. Dilation was performed with morphological operations. The voxels in the PET images belonging to those (enlarged) physiological regions were set to a SUV of zero.

Additionally, physiological organs in the head region, such as the parotids, salivary glands, and lacrimal glands, were identified as exhibiting PSMA uptake without any lesions detected in our database. To enhance the precision of our analysis and for practical considerations, a decision was made to exclude the signal above the skull. The voxels above the lowest voxel of the skull were set to SUV of zero.

The second step to consider the final TTV is to account for noise in the PET image. This is performed with a SUV threshold as proposed by Gafita et al. [17]. This threshold is based on the liver uptake of the entire cohort but is individually adjusted to each patient's uptake. The formula used is as follows:

$$SUV_{thr} = \frac{SUV_{mean cohort}}{SUV_{mean}} \cdot (SUV_{mean} + SD)$$

Where the SUV<sub>mean cohort</sub> represents the average liver SUV mean across the 139 males of the database. The SUV<sub>mean</sub> and the SD are the individual liver SUV mean and the standard deviation respectively.

Additionally, a specific subregion of the TTV, the TTV<sub>Bone</sub> (TTVb), is analyzed. This focus is justified by two main considerations: first, the majority (96%) of patients in the cohort present with bone metastases, and second, there is no physiological uptake in the bones. This TTV<sub>Bone</sub> is thus obtained by setting SUV=0 to all voxels that do not belong to one of the bone structures extracted by TotalSegmentator. The list of bone structure are: all vertebrae (sacrum, L, T, C), all ribs, femurs, claviculars, hips, humeri, and scapulae.

At the end of this process, we obtained two PET VOI, TTV and TTV<sub>Bone</sub>, where SUV features will be extracted. An illustration of this workflow is depicted in the Fig. 3

## Feature extraction

Feature extraction focused on deriving metrics from imaging data and clinical variables to model the patient response to <sup>177</sup>Lu-PSMA therapy. The imaging-based feature extraction were first order statistics extracted from the TTV VOIs, including Mean, Median, and Mode, as well as variability descriptors like Standard Deviation (Std. dev.), Skewness, and Kurtosis. Given the high prevalence of bone metastases in prostate cancer, bone-specific first-order statistics were also calculated to evaluate the metastatic burden in skeletal tissues. These metrics provide lesion information, as physiological uptake in bone tissue is absent, thereby minimizing the risk of misclassification.

Additionally, liver-specific metrics, including the liver's SUV mean, SUV max, and SUV std. dev., were extracted to provide further insights into organ-specific tumor involvement. The inclusion of these metrics aims to keep information of the liver uptake as it is not included in the TTV even when there are liver metastases. The kidney to tumor ratio is also used.
<figure><img src="image_6.png" /><figcaption>**Fig. 3** ⁶⁸Ga-PSMA PET/CT's TTV image processing stages. The image on the left is the original ⁶⁸Ga-PSMA PET/CT image in SUV units. As shown by the red arrow, the image presents physiological uptake. The centered whole body image corresponds to the image after removal of physiological uptake. Next on the right, the final processed image without noise and bone masking for improved visualization of the TTVb. The zoomed pelvis of the images in the center and on the right show the removal of the noise: top and bottom respectively</figcaption></figure>

Clinical variables, such as the pre-treatment PSA level, the hemoglobin level before treatment, whether the patient has had chemotherapy, the age at the start of the treatment, the Karnofsky score, and the time between the diagnosis and the start of the treatment, were incorporated to enhance the predictive model. These variables complement imaging features by capturing essential aspects of patient health, disease progression, and treatment history.

## Statistical analysis

The TTVs were calculated across the entire dataset. To evaluate the predictive capability of TTV, the distribution of SUVmean within the TTV was compared between the responder and non-responder groups using the Wilcoxon-Mann-Whitney test. Statistical significance was set at *p* < 0.05, with rejection of the null hypothesis indicating a significant difference between the groups. To enhance the reliability of the results, we applied a multiple-comparison correction to mitigate the increased risk of false positives arising from testing multiple features. Specifically, we used the Benjamini-Hochberg procedure [18], which ranks all p-values in ascending order and calculates an adaptive significance threshold for each test as (i/m)α where i is the rank, m is the total number of tests, and α is the chosen significance level. The largest p-value meeting the threshold determines the overall significance cut-off.

To further assess the robustness of the methodology, an ablation study is presented. The study was performed on both the subtraction and threshold parameters. This included:

* Generating a TTV with a threshold applied but without subtracting physiologically uptaking organs.

* Generating a TTV calculated without applying any threshold.
Additionally, an intercorrelation analysis was conducted to examine pairwise correlations among features, enabling the detection and removal of highly correlated (redundant) variables.

## Classifiers

To predict patient response to ¹⁷⁷Lu-PSMA therapy, we implemented five classifiers from the scikit-learn library [19]. The classifiers included a Multilayer Perceptron (MLP), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Random Forest (RF), and LASSO (Least Absolute Shrinkage and Selection Operator) regression model. Each classifier was selected for its unique strengths in handling different types of data structures and relationships. The MLP captures complex nonlinear relationships in the data. The GNB classifier is a probabilistic model based on Bayes' theorem providing interpretable predictions, assuming feature independence. The SVM classifier is a classifier for high-dimensional data, using kernel functions to handle nonlinear decision boundaries. The RF classifier, an ensemble method, combines multiple decision trees to enhance predictive accuracy and robustness against overfitting. Finally, LASSO regression performs feature selection by penalizing the absolute size of the coefficients, which is useful in handling high-dimensional datasets with potentially irrelevant features. Each model was trained and validated using 5-fold cross-validation to ensure reliable generalization to unseen data. This method segment the database in five folds, the model is trained with four folds and tested with the remaining one. This is done five times so as for the model to be trained and tested with every patient. Only the features whose p-values are above the significative corrected-threshold were selected as input to the different models. Additionally, a Pearson correlation analysis was performed to assess pairwise relationships among features, and variables with an absolute correlation coefficient greater than 0.85 were considered redundant and removed.

An optimization technique was used to select the better classifier with the most performing hyperparameters. We used the Optuna package [20] to run an intelligent grid search across the above-described classifiers. The metric to optimize was the F1-score and the optimization was performed across 250 trials. The SHAP (SHapley Additive exPlanations) values [21] were used to assess the understanding of the model.

The performances of the predictive models were evaluated using the F1 score, the Area Under the Curve (AUC), and the Receiver Operating Characteristic (ROC) curve. The F1 score combines precision and recall into a single measure, providing a balanced evaluation of the model's ability to correctly classify positive cases while minimizing false positives and false negatives.

$$F_1 = 2 \times \frac{precision \times recall}{precision + recall}$$

The AUC quantifies the overall performance of the model by measuring the area under the ROC curve, which plots the true positive rate (sensitivity) against the false positive rate (1-specificity) across different decision thresholds.

The ROC curve of the proposed classifiers were compared with the ROC curve obtained by applying the nomogram published by Gafita et. al [22] to our dataset. The predictions were obtained by introducing the data of our patients in the nomogram that
is available online .<fnref n="1" /> The inputs of the nomogram are the time since diagnosis, whether the patient has received chemotherapy, the hemoglobin level, the tumor SUVmean, whether the number of metastases is lesser than twenty, and whether the patient has pelvic lymph nodes, bones or liver metastases. The information regarding the type of lesions and the number of lesions were obtained manually. The prediction of the PSA50 response is used to compute the ROC curve. We used the F1-score metric to both our models and Gafita et al.'s nomogram.

Results

Correlations

In Fig. 4, the correlation analysis highlights the relationships between the features. As expected, TTV Volume and TTVb Volume are highly correlated, as are SUV measures like TTV Max, Mean, and Median, reflecting consistent patterns in tumor activity. In contrast, clinical features such as Karnofsky performance score, age, and chemotherapy status showed weaker correlations with the image-derived features. Similarly, liver activity measures like Liver Std. dev. (SUV) had limited correlation with tumor metrics, suggesting they capture different aspects of physiology. The Ratio Tumor/Kidney metric also exhibited weak correlations across most variables, indicating it provides unique information. Overall, image-derived features are closely interconnected, while clinical parameters appear less strongly linked, emphasizing the complementary nature of these

<figure><img src="image_7.png" /><figcaption>Fig. 4 Correlation matrix of the imaged-derived features and biological features. This is a symmetric matrix</figcaption></figure>

<footnote n="1">¹https://www.uclahealth.org/departments/urology/iuo/research/prostate-cancer/lupsma-response-prediction-tool</footnote>
features. TTVb Volume (cc), TTV Median (SUV), TTVb Median (SUV), TTV Std. dev. (SUV), and TTVb Std. dev. (SUV), exhibited strong pairwise correlations ($|r| > 0.85$) and were therefore excluded from model training to avoid redundancy and mitigate multicollinearity, more information is given on the supplementary material (Table 2).

The relationship between individual features and the PSA50 target is visualized in Fig. 5. Features with statistically significant associations (p-value < 0.011) include statistics of the TTV and TTVb, the time since the diagnosis and the ALP pre-treatment.

The two most significative correlations (p-value < 0.0001) between one single feature and the PSA50 response were the standard deviation of the TTV and TTVb and are shown as an example in the boxplots in Fig. 6.

## Prediction

Among the five trained, optimized and compared classifiers, we report here the two best ones: the LASSO (Logistic regression with L1) and the SVM. The LASSO classifier achieved an F1-score of 0.68. The SVM has an F1-score of 0.67. The ROC curves are presented in Fig. 7, illustrating the classifiers' performance. As an illustrative example, the LASSO model predicted a 99.0% probability of achieving a PSA50 response for the patient shown in Fig. 3, who ultimately experienced a 79% reduction in PSA. Additionally, feature importance for the LASSO model was analyzed using SHAP values, which are visualized in Fig. 8. The SHAP values quantify the impact of each feature on the predictions of the LASSO model. The feature "TTVb Mean" has the highest impact on the model's predictions. According to the model, higher TTVb mean tend to augment the predicted PSA50 response probability.

**Ablation study** We performed an ablation study to determine if all steps proposed in the TTV extraction are necessary. Detailed results are provided in the supplementary material (Figs. 9 and 10). In summary, we show that the correlations between the TTV

<figure><img src="image_8.png" /><figcaption>**Fig. 5** Comparison of image-derived and clinical features based on statistical significance of their correlation with PSA50: this bar plot displays the -log10 transformed p-values of various features, with image-derived features shown in blue and clinical features in orange. The red dashed line represents the original significance threshold ($\alpha = 0.05$), while the purple dashed line indicates the Benjamini-Hochberg-corrected threshold (BH = 0.011) for multiple comparisons. Features above these thresholds are considered statistically significant: they are the most correlated with PSA50 change</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 6** Mann-Whitney-U test correlation between the standard deviation of the TTV and the TTVb with respect to the biological response (PSA50)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 7** Receiver Operating Characteristic (ROC) curves comparing the performance of the trained LASSO (red), the SVM model (blue) with a reference Nomogram model (green). The ROC curve illustrates the trade-off between True Positive Rate (sensitivity) and False Positive Rate (1 - specificity) across different classification thresholds. The diagonal gray line represents random guessing. The Area Under the Curve (AUC) and F1 score for each model are displayed in the legend, indicating the predictive power of each approach</figcaption></figure>

derived features and the biological response to the treatment are reduced when not all steps of the TTV extraction are performed.

*Discarded classifiers* The results of the other classifiers are shown in the supplementary material (Fig. 11). The results are similar to the classifiers shown with respect to AUC and the F1-score.
<figure><img src="image_11.png" /><figcaption>**Fig. 8** SHAP (SHapley Additive exPlanations) "beeswarm" plot showing the impact of each feature on the predictions of the LASSO model. Each dot represents an individual patient, with color indicating the feature value (red = high, blue = low). Features are sorted by their average impact, with higher-ranking features exerting more influence on model predictions. A positive SHAP value pushes predictions towards a higher probability of response (responder according to the PSA50 threshold), while a negative value reduces it</figcaption></figure>

## Discussion

The database, comprising 139 patients from real-world clinical setting, offers insights that, while not universally generalizable to all hospitals or patients, provide valuable and practical relevance for a variety of clinical contexts. The correlation analysis revealed strong intercorrelations among many image-derived features, largely due to their common dependence on tumor burden and lesion uptake. To address this, highly correlated features were excluded prior to model training, reducing redundancy and improving model stability and generalization. For each pair of highly correlated features, one was removed; the choice of which feature to retain had minimal impact on overall model performance. These image-related features were significantly correlated with PSA50 response, a finding consistent with prior studies in the literature, including those by Kuo et al. [23], which also report strong associations between TTV's SUV mean or dose metrics and treatment response. Additionally, the time since diagnosis emerged as another highly significant predictor of treatment response. The SHAP values suggest that the longer the time since diagnosis, the smaller the probability of response. However, it is important to note that a feature's impact on the model does not necessarily reflect its raw correlation with the clinical outcome. For example, the TTVb max is positively correlated with biological response, yet its SHAP values suggest an opposite effect on the model's predictions. This counterintuitive behavior likely results from how the LASSO model handles multicollinearity by redistributing weights among correlated features.

The classifiers used present comparable results to the nomogram published by Gafita et al. [22] that is used as a reference. One advantage of our method compared to the one published by Gafita et al. is the automation and reproducibility. The TTV extraction method provides the image-derived features automatically without the need of expert time. This fact makes it potentially more scalable and ready to use for larger databases.
The use of kidney to tumor ratio has been tested like in the paper of Hohberg et al [24]. The feature does not appear as the most influential ones according to the Shapley values even if the correlation to biological response is significant.

In the results published by Moazemi et al. [25], an AUC of approximately 80% was reported, which is comparable to the 78% AUC achieved by the LASSO model in this study. The main methodological difference lies in their use of individual lesions for prediction, whereas we adopted a total tumor volume approach. While the lesion-based strategy may offer slightly higher granularity, it requires manual delineation of each lesion, which is time-consuming and less practical in clinical workflows.

When comparing PSA50 response rates, our cohort achieved a PSA50-RR of 44.6%, which is comparable to the rates reported in major trials: 46% in VISION and 66% in TheraP. These differences highlight the heterogeneity of patient populations and treatment contexts, reinforcing the need for external validation before clinical adoption.

One limitation of the proposed TTV extraction method is that lesions located within organs excluded during the preprocessing step are also removed. This is particularly relevant for liver metastases (11/139 cases). Although liver-related features such as maximum, mean, and standard deviation uptake were analyzed, they were not included as inputs to the classifiers because they did not meet the significance threshold. In addition, discrepancies between PET and CT scans can introduce segmentation errors. For example, variations in bladder filling between the CT and the PET acquisitions can lead to inaccuracies, as segmentation is performed on the CT image but applied to the PET scan (an example is provided in the supplementary material, Fig. 12). Furthermore, certain physiological structures that exhibit natural PSMA uptake, such as the lacrimal glands, salivary glands, and ureters, are not included in the TotalSegmentator segmentation. As a consequence, the head must be excluded, inadvertently removing potential lesions. Similarly, in some patients with significant ureteral uptake, this physiological signal is mistakenly incorporated into the TTV. Another limitation arises when enlarging the masks of physiological organs during preprocessing, as this can inadvertently exclude adjacent lesions from the TTV. An example of this issue is illustrated in the supplementary material (Fig. 13). While manual corrections could address these artifacts, they would undermine the objective of achieving a fully automated workflow. Despite these known limitations, the proposed simple TTV extraction method provides an estimate of the overall metastatic burden across the cohort and enables the generation of high-quality features for classifier development. Specific validation of TTV accuracy against manual delineation is currently lacking but remains particularly challenging due to the inherent variability and uncertainty in physician contours.

The current model requires external and prospective validation, ideally in multi-center cohorts, before it can be considered for clinical deployment. Such validation should include additional clinically meaningful endpoints such as overall survival (OS) and progression-free survival (PFS), in addition to PSA-based metrics, to ensure robustness and generalizability. This is particularly important given the well-documented limitations of PSA50 as a surrogate marker, including its inconsistent correlation with survival outcomes, variability across studies, and susceptibility to phenomena such as PSA flare.

From a practical perspective, the proposed workflow is relatively efficient: the entire pipeline, including segmentation, feature extraction, and prediction, takes approximately 40 min per patient on standard hardware, the majority of this time is used to compute
the segmentations with TotalSegmentator and mainly depends on the GPU speed. This level of automation significantly reduces the need for expert intervention and makes the approach feasible for integration into clinical research workflows. In a clinical setting, the model could serve as a decision-support tool rather than a strict gatekeeper. A high predicted probability of non-response might prompt clinicians to consider alternative treatment strategies earlier, such as switching to chemotherapy or enrolling the patient in clinical trials, rather than proceeding with Lu-PSMA therapy. Conversely, patients predicted to respond could be prioritized for therapy initiation.

## Conclusion

This study demonstrates the feasibility of an automated pipeline for extracting Total Tumor Volume (TTV) from ⁶⁸Ga-PSMA PET/CT images, thereby reducing reliance on manual intervention. By leveraging real-world data from a monocentric cohort, the proposed approach enhances the clinical relevance of predictive modeling and provides insights into treatment response variability. Among the evaluated machine learning models, LASSO and SVM classifiers achieved promising performance in predicting PSA50 response, with results comparable to established nomograms. These findings highlight the potential of automated, image-based biomarkers to support personalized treatment strategies. Future work should focus on multi-center validation, incorporation of survival endpoints (OS, PFS), and further refinement of predictive models to maximize clinical utility and facilitate integration into routine practice.

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s40658-025-00808-6.

Supplementary Material 1

Supplementary Material 2

## Acknowledgements

This work was performed within the framework of the SIRIC LYRICAN Grant INCa-INSERM-DGOS-12563, the LABEX PRIMES (ANR-11-LABX-0063) of Université de Lyon, within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the ANR, and CIFRE with Siemens Healthcare SAS (ANRT). This work was granted access to the HPC resources of IDRIS under the allocation 2019-101203 made by GENCI (Jean Zay computing center). We thank Reviewer 1 for his pertinent comments.

## Author contributions

ERS (Corresponding Author) developed the model, conducted data analysis and evaluation, and prepared the manuscript. ALG, ASL, VP and EP were responsible for acquiring the clinical data. JNB and DS supervised the study. DS contributed significantly to the study design and methodologies. TB made a substantial contribution to the creation of the database and assisted with the deployment of TotalSegmentator. DS made substantial revisions to the manuscript. All authors contributed to the study's conception and design, read, and approved the final manuscript.

## Funding

The authors received no specific funding for this study beyond their regular salaries. ERS, the corresponding author, is supported by Siemens Healthineers through a PhD contract. JNB and DS are employed by the Centre Léon Bérard and the CNRS respectively. ALG, ASL, VP are physicians employed by the Centre Léon Bérard and EP is a physician employed by the Centre Hospitalier de Saint Denis.

## Data availability

Raw Reproducibility data generated or analysed during this study are included in this published article. Original patient data are not publicly available due to ethics reasons but are available from the corresponding author on reasonable request.

## Declarations

### Ethical approval and consent to participate

Informed consent was obtained from all participants, and all patient data were collected in accordance with established ethical guidelines and regulations.
**Consent for publication**

All participants provided written informed consent.

**Competing interests**

The authors state that they have no Conflict of interest to disclose.

Received: 29 April 2025 / Accepted: 1 October 2025

Published online: 11 November 2025

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