# Deep learning combines MRI texture and clinical factors to predict early efficacy in TACE for hepatocellular carcinoma

Xiaole Fan, Yuhang Wang, Xu Sun, Jushun Yang, Jianan Cai, Ming Zhang, Loïc Cabannes, Trung Hieu Ngo, Tianle Wang, Li Zhu, et al.

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Xiaole Fan, Yuhang Wang, Xu Sun, Jushun Yang, Jianan Cai, et al. Deep learning combines MRI texture and clinical factors to predict early efficacy in TACE for hepatocellular carcinoma. Oncologie, 2025, 10.1515/oncologie-2025-0428. hal-05520557

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Xiaole Fan, Yuhang Wang, Xu Sun, Jushun Yang, Jianan Cai, Ming Zhang, Loïc Cabannes, Trung Hieu Ngo, Tianle Wang, Li Zhu, Meihong Sheng, Fangrong Yan, Feng Feng* and Bosheng He*

# Deep learning combines MRI texture and clinical factors to predict early efficacy in TACE for hepatocellular carcinoma

https://doi.org/10.1515/oncologie-2025-0428

Received October 5, 2025; accepted November 28, 2025;

published online December 12, 2025

## Abstract

**Objectives:** Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, and transarterial chemoembolization (TACE) represents the standard treatment for patients with intermediate-stage disease. However, early prediction of post-TACE response remains challenging due to tumor heterogeneity and limited interpretability of existing models. This study aimed to develop and validate a deep learning (DL) framework that integrates multi-sequence MRI texture features with pre-operative clinical factors to predict early postoperative efficacy following TACE.

**Methods:** In this multicenter retrospective study, 178 HCC patients who underwent TACE were enrolled from two institutions. Dynamic contrast-enhanced MRI data and clinical variables were analyzed to construct a Transformer-based DL model incorporating a multi-head attention mechanism

for sequence-level feature integration. The model's prediction probability and significant clinical factors were further combined through logistic regression to generate a nomogram for individualized response estimation.

**Results:** The DL model achieved robust predictive performance, with an area under the curve (AUC) of 0.90 in the internal testing set and 0.79 in the external validation set. Incorporating clinical variables improved predictive accuracy, yielding AUCs of 0.98 and 0.86 for the in-house and external datasets, respectively. Calibration and bootstrap analyses confirmed model stability, while decision curve analysis demonstrated superior clinical utility compared with conventional approaches. Attention-weight analysis revealed that T2-weighted and diffusion-weighted sequences contributed most to prediction, consistent with their biological relevance in tumor viability assessment.

**Conclusions:** The integrated DL nomogram provides an interpretable, noninvasive tool for predicting early TACE response in HCC, potentially aiding in personalized therapeutic decision-making and optimizing treatment strategies.

**Keywords:** hepatocellular carcinoma; magnetic resonance imaging; deep learning; therapeutic chemoembolization; tumor activity

Xiaole Fan, Yuhang Wang and Xu Sun equally as co-first authors.

*Corresponding authors: Feng Feng, Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, 226361, China, E-mail: fengfeng@ntu.edu.cn; and Bosheng He, Department of Radiology, Southeast University Affiliated Nantong First People's Hospital, Nantong, 226001, China; and Clinical Medicine Research Center, Southeast University Affiliated Nantong First People's Hospital, Nantong, 226001, China, E-mail: Bosheng_he@ntu.edu.cn

Xiaole Fan, Jushun Yang, Jianan Cai, Tianle Wang, Li Zhu and Meihong Sheng, Department of Radiology, Southeast University Affiliated Nantong First People's Hospital, Nantong, China

Yuhang Wang and Fangrong Yan, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China

Xu Sun, UMR GREYC, Normandie Univ, UNICAEN, ENSICAEN, CNRS, Caen, France

Ming Zhang, Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, China

Loïc Cabannes, UFR de Mathématique et Informatique, Faculté de Sciences, Université Paris Cité, Paris, France

Trung Hieu Ngo, Laboratoires des Sciences Numeriques de Nantes, Nantes Université, Nantes, France

## Introduction

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, accounting for over 400,000 new cases annually in China. It ranks as the sixth most common malignancy in the United States and fourth locally, and is the second leading cause of cancer-related mortality in China [1, 2]. Although surgical interventions offer potential for long-term survival, fewer than 30 % of patients are eligible for curative treatments like transplantation, resection, or ablation [2, 3].

The Barcelona Clinic Liver Cancer (BCLC) staging system recommends transarterial chemoembolization (TACE) as the treatment of choice for intermediate-stage HCC, especially for patients with unresectable multinodular tumors without extrahepatic spread. In Asia, especially in China, TACE is often recommended for a broader range of scenarios [4], and
the China liver cancer staging scheme recommends TACE as a treatment method for patients in stages Ib to IIIB [5]. While TACE improves survival rates, its efficacy varies significantly, influenced by tumor burden, baseline liver function, tumor biology, and procedural details [6]. Additionally, TACE can trigger the release of angiogenic factors like vascular endothelial growth factor, potentially increasing the risk of tumor recurrence and progression [7, 8].

Recent evidence shows that initial treatment responses to TACE predict improved progression-free and overall survival rates [9–11]. This highlights the importance of identifying patients who are likely to respond early, as early response serves as an objective surrogate marker for long-term benefit. Previous radiomics-based studies have primarily focused on predicting long-term outcomes, such as time-to-progression or overall survival. For instance, Wang et al. developed an EfficientNetV2-based model for evaluating time-to-progression and overall survival (OS) prognosis of TACE in treatment-naïve, intermediate-stage HCC; patients with lower scores were found to benefit from TACE [12]. However, long-term prognostic models are influenced by multiple post-treatment variables and do not directly reflect the tumor's intrinsic sensitivity to embolization. In contrast, predicting early post-TACE response using preoperative MRI can provide actionable insights for treatment planning before intervention.

Image texture analysis offers a viable approach by extracting quantitative data on tumor heterogeneity through analyses of gray-level histograms and pixel distributions [13, 14]. Studies using machine learning techniques based on radiomics have demonstrated significant predictive power for liver cancer outcomes [15–19]. Compared to traditional machine learning models, deep learning (DL) models have been widely applied in clinical diagnosis and prognostic decision-making related to medical imaging due to their strong learning capabilities for complex data [20–23]. To investigate DL's effectiveness in predicting early treatment responses after TACE, we conducted a multicenter study aiming to develop and validate a Transformer-based DL model that combines magnetic resonance imaging (MRI) radiomics and clinical risk factors to predict early post-TACE responses in HCC patients.

# Materials and methods

## Study design and patient participants

This multicenter retrospective study included 178 HCC patients treated with TACE at two institutions: The Southeast

University Affiliated Nantong First People's Hospital (SUANFPH) and the Affiliated Tumor Hospital of Nantong University (ATHNU). The in-house dataset from SUANFPH (January 2014 to April 2024) comprised 136 cases for training and internal testing. An independent validation set from ATHNU (January 2021 to April 2024) included 42 cases for model evaluation. Ethics approval was obtained from the Ethics Committee of Nantong First People's Hospital (Approval No. 2023KT174) and the Ethics Committee of Nantong Tumor Hospital (Approval No. 2022-067). The requirement for informed consent was waived because this study was a retrospective analysis of existing medical records, involved no interventions, and all patient data were anonymized and handled confidentially in accordance with institutional privacy protocols. The research team had no direct contact with patients during the study period. The study adheres to the ethical standards of the Declaration of Helsinki.

Participants were diagnosed with HCC *via* AASLD non-invasive criteria or pathological histology [24]. Inclusion criteria were: (1) age≥18 years; (2) lesion diameter≥1 cm; (3) preoperative dynamic contrast-enhanced MRI within 7 days before TACE; (4) first MRI follow-up between 1 and 3 months post-TACE [5]; (5) Child-Turcotte-Pugh (CTP) class A or B liver function; (6) complete clinical data and adherence to follow-up. From 1,797 patients, 1,619 were excluded due to lack of necessary imaging or data (n=741), inadequate follow-up (n=690), surgical resection (n=111), ablation therapy (n=40), other primary malignancies (n=3), or infiltrative lesions with unclear boundaries (n=34) (Figure 1A).

## MRI acquisition

MRI scans were performed using 1.5 T and 3.0 T Siemens (Verio, SymphonyTim, Prisma, Espree, Germany) and Philips (Ingenia CX, Netherlands) scanners, employing contrast agents such as Gadoteric Acid Meglumine, Dimeglumine Gadopentetic Acid, and Gadoxetic Acid Disodium. Imaging protocols included T2-weighted fat suppression imaging (T2WI), gradient-echo T1-weighted imaging (in-phase and out-of-phase), diffusion-weighted imaging (DWI) (b-values 0 and 800 mm²/s), and dynamic fat-suppressed spoiled gradient-echo T1-weighted sequences pre- and post-contrast. Bolus-tracking captured late arterial phase (AP) (18–36 s post-injection) and portal venous phase (PVP) (50–90 s post-injection); delayed phase (DP) was acquired 3–5 min post-contrast. For Gadoxetic Acid Disodium, hepatobiliary phase images were taken at 10 and 15 min post-injection. Detailed parameters are in Supplemental Materials (Supplementary Table S1, 2).
<figure><img src="image_3.png" /><figcaption>**Figure 1:** Inclusion and exclusion criteria and the algorithm process. (A) Flow chart showing the inclusion and exclusion criteria of the study. (B) Study design of using a classification algorithm process for multi-sequence MRI images.</figcaption></figure>

## Treatment

TACE was performed by interventional radiologists with over 10 years of clinical experience, utilizing the Seldinger technique for femoral artery access. A 5-F RH catheter (RF\*DG35008M, TERUMO, Japan) was employed to facilitate angiography of the superior mesenteric or common hepatic artery, enabling the assessment of vascular anatomy and tumor characteristics. The chemoembolization procedure involved the slow infusion of letaparitide (2–4 mg), followed by superselective catheterization using a 2.7-F microcatheter (Progreat MC-PE27131, TERUMO, Japan). The chemoembolic agents varied depending on the case:

- In 164 cases, a lipiodol emulsion containing epirubicin (20–80 mg), oxaliplatin (50–100 mg), or lobaplatin (20–50 mg) mixed with iodized oil (5–20 mL) was administered.

- In 14 cases, drug-eluting microspheres loaded with epirubicin (60–100 mg) and polyvinyl alcohol microspheres (0.5–1 g) were utilized.

Additional embolization was performed using polyvinyl alcohol particles (100–500 µm) or gelatin sponge particles saturated in nonionic contrast medium. The quantity of embolic material was determined by the radiologist, with the goal of achieving complete cessation of blood flow in the tumor-feeding arteries.
## Follow-up and assessment of post-TACE response

Post-treatment imaging evaluations were independently conducted by two radiologists, Reader 1 and Reader 2, both with over five years of diagnostic experience. Any discrepancies between their assessments were resolved by a third senior radiologist, Reader 3, who has more than 10 years of expertise in abdominal imaging. The largest liver lesion was identified as the target lesion and analyzed using the LI-R-ADS Treatment Response Assessment (TRA) version 2024 algorithm during the first post-TACE MRI [25]. Lesion responses were categorized as follows:

- LR-TR Viable: The defining characteristic was the presence of mass-like enhancement (regardless of degree or phase) within the treated lesion or along its margin. Such enhancement could manifest as nodular, smooth, or irregular patterns.

- LR-TR Nonviable: No mass-like enhancement (irrespective of degree or phase) was observed either within the treated lesion or along its margin.

- LR-TR Equivocal: Lesions were categorized as equivocal when there was uncertainty regarding the presence or morphology of mass-like enhancement. To clarify their status, one of the following methods was employed:

(a) If restricted diffusion (of any degree) or mild-to-moderate T2 hyperintensity was detected in the corresponding area, the lesion could be upgraded to LR-TR Viable;

(b) Digital subtraction angiography (DSA) performed within 7 days after MRI, with tumor staining indicating viability [26];

(c) Follow-up imaging at 3 months post-treatment, where progression suggested viability, while stability indicated non-viability.

## Image segmentation and texture feature extraction

Segmentation and texture feature extraction from preoperative dynamic contrast-enhanced MRI were performed using the Deepwise Medical Multimodality Research Platform across six image sequences: Pre-contrast T1-weighted imaging (T1WI), T2WI, AP, PVP, DP, and DWI. Reader 1 manually outlined regions of interest (ROI) for the target lesion in each image sequence using AI-assisted semi-automatic tools; segmentation accuracy was reviewed by Reader 3. Because this verification process involved quality

control rather than independent re-segmentation, inter-observer agreement metrics such as Cohen's kappa or intraclass correlation coefficient were not applicable. Nonetheless, this two-step review ensured high segmentation reliability and minimized potential variability. Both readers were blinded to the patient outcome at the time of segmentation.

From the segmented ROIs, 1,498 radiomic features were extracted per sequence, covering seven categories: first-order statistics, shape descriptors, gray-level co-occurrence matrix (GLCM), gray-level size zone matrix (GLSZM), gray-level run length matrix (GLRLM), gray-level dependence matrix (GLDM), and neighboring gray tone difference matrix (NGTDM) features. Gray-level discretization was conducted using a fixed bin width of 5 to ensure consistent intensity scaling across MRI sequences. To enhance multi-scale texture representation, a comprehensive set of embedded image filters was applied, including wavelet, square, square root, logarithm, exponential, gradient, local binary pattern (LBP2D and LBP3D), and Laplacian of Gaussian (LoG) filters with sigma values ranging from 1 to 5. These preprocessing settings follow PyRadiomics recommendations and improve the reproducibility and robustness of extracted features across heterogeneous MRI datasets.

## Data preprocessing

Radiomic features from MRI images of the in-house dataset (n=136) were standardized via z-score normalization for each sequence. To avoid sparse categories, albumin-bilirubin (ALBI) grades 2 and 3 were combined. Thirty-two potential clinical predictive factors were analyzed, identifying 11 significant factors for modeling (Table 1): portal/hepatic vein invasion (PVTT), tumor size (T), intratumor necrosis, intratumor hemorrhage, corona enhancement, diameter of viable lesions, D-dimer, glutamic pyruvic transaminase (ALT), glutamyltranspeptidase (GGT), alkaline phosphatase (ALP), and ALBI grade.

Missing data were imputed using mean values for continuous factors and median values for categorical factors. The overall proportion of missing data among the 11 clinical variables was low (0–2.9 %), with D-dimer showing the highest rate (2.9 %) and most other variables showing no missingness. Given this minimal level of missing data, mean/median imputation was deemed appropriate and unlikely to introduce significant bias. Continuous factors underwent z-score normalization; categorical factors were converted to dummy variables.

The dataset was split into training (75 %) and internal testing (25 %) sets. To ensure balanced outcome representation during model evaluation, a five-fold stratified cross-validation
<table><caption>**Table 1:** Distribution of model characteristics across training and internal testing datasets.</caption><thead><tr><th rowspan="2">Characteristic</th><th colspan="2">In-house dataset</th><th rowspan="2">p-Value</th></tr><tr><th>Training</th><th>Testing</th></tr></thead><tbody><tr><td>Number of patients</td><td>102</td><td>34</td><td></td></tr><tr><td>Age, mean, SD</td><td>64.45 (10.63)</td><td>62.71 (10.85)</td><td>0.411</td></tr><tr><td>ADC, mean, SD</td><td>1,028.45 (323.15)</td><td>1,025.89 (344.04)</td><td>0.969</td></tr><tr><td>Tumor size, mean, SD</td><td>3,835.57 (4,269.97)</td><td>4,397.92 (4,030.00)</td><td>0.501</td></tr><tr><td>Diameter of viable lesions, mean, SD</td><td>50.46 (32.65)</td><td>55.24 (34.68)</td><td>0.468</td></tr><tr><td>Sex, n, %</td><td></td><td></td><td>0.782</td></tr><tr><td>Male</td><td>87 (85.3)</td><td>30 (88.2)</td><td></td></tr><tr><td>Female</td><td>15 (14.7)</td><td>4 (11.8)</td><td></td></tr><tr><td>Number of tumors, n, %</td><td></td><td>0.578</td><td></td></tr><tr><td>1</td><td>56 (54.9)</td><td>22 (64.7)</td><td></td></tr><tr><td>2</td><td>18 (17.6)</td><td>3 (8.8)</td><td></td></tr><tr><td>3</td><td>4 (3.9)</td><td>2 (5.9)</td><td></td></tr><tr><td>&gt;3</td><td>24 (23.5)</td><td>7 (20.6)</td><td></td></tr><tr><td>Target lesion location, n, %</td><td></td><td>0.106</td><td></td></tr><tr><td>Right lobe of the liver</td><td>78 (76.5)</td><td>25 (73.5)</td><td></td></tr><tr><td>Left lobe of the liver</td><td>21 (20.6)</td><td>5 (14.7)</td><td></td></tr><tr><td>Cross lobar</td><td>2 (2.0)</td><td>4 (11.8)</td><td></td></tr><tr><td>Caudate lob</td><td>1 (1.0)</td><td>0 (0.0)</td><td></td></tr><tr><td>BCLC stage, n, %</td><td></td><td>0.394</td><td></td></tr><tr><td>0</td><td>7 (6.9)</td><td>3 (8.8)</td><td></td></tr><tr><td>A</td><td>40 (39.2)</td><td>17 (50.0)</td><td></td></tr><tr><td>B</td><td>30 (29.4)</td><td>10 (29.4)</td><td></td></tr><tr><td>C</td><td>25 (24.5)</td><td>4 (11.8)</td><td></td></tr><tr><td>Child-Turcotte-Pugh, n, %</td><td></td><td></td><td>0.494</td></tr><tr><td>A</td><td>94 (92.2)</td><td>30 (88.2)</td><td></td></tr><tr><td>B</td><td>8 (7.8)</td><td>4 (11.8)</td><td></td></tr><tr><td>Blood supply, n, %</td><td></td><td></td><td>0.730</td></tr><tr><td>Single blood supply artery</td><td>92 (90.2)</td><td>32 (94.1)</td><td></td></tr><tr><td>Multiple blood supply arteries</td><td>10 (9.8)</td><td>2 (5.9)</td><td></td></tr><tr><td>ALBI grade, n, %</td><td></td><td></td><td>0.202</td></tr><tr><td>1</td><td>73 (71.6)</td><td>20 (58.8)</td><td></td></tr><tr><td>2 or 3</td><td>29 (28.4)</td><td>14 (41.2)</td><td></td></tr><tr><td>Cirrhosis, n, %<sup>a</sup></td><td>38 (37.3)</td><td>18 (52.9)</td><td>0.114</td></tr><tr><td>Ascites, n, %<sup>a</sup></td><td>8 (7.8)</td><td>5 (14.7)</td><td>0.310</td></tr><tr><td>Fat in mass, n, %<sup>a</sup></td><td>39 (38.2)</td><td>12 (35.3)</td><td>0.839</td></tr><tr><td>Arteriovenous fistula, n, %<sup>a</sup></td><td>8 (7.8)</td><td>1 (2.9)</td><td>0.450</td></tr><tr><td>Portal/hepatic vein invasion, n, %<sup>a</sup></td><td>25 (24.5)</td><td>4 (11.8)</td><td>0.149</td></tr><tr><td>Intratumor necrosis, n, %<sup>a</sup></td><td>55 (53.9)</td><td>16 (47.1)</td><td>0.554</td></tr><tr><td>Intratumor hemorrhage, n, %<sup>a</sup></td><td>37 (36.3)</td><td>17 (50.0)</td><td>0.164</td></tr><tr><td>Corona enhancement, n, %<sup>a</sup></td><td>27 (26.5)</td><td>9 (26.5)</td><td>&gt;0.99</td></tr><tr><td>Alpha fetoprotein, n, %<sup>b</sup></td><td>76 (74.5)</td><td>22 (64.7)</td><td>0.278</td></tr><tr><td>Prothrombin time, n, %<sup>b</sup></td><td>8 (7.8)</td><td>1 (2.9)</td><td>0.450</td></tr><tr><td>Glutamic oxaloacetic transaminase, n, %<sup>b</sup></td><td>54 (52.9)</td><td>20 (58.8)</td><td>0.691</td></tr><tr><td>Lactic dehydrogenase, n, %<sup>b</sup></td><td>43 (42.2)</td><td>17 (50.0)</td><td>0.434</td></tr><tr><td>Cholinesterase, n, %<sup>b</sup></td><td>20 (19.6)</td><td>8 (23.5)</td><td>0.630</td></tr><tr><td>Total bilirubin, n, %<sup>b</sup></td><td>49 (48.0)</td><td>21 (61.8)</td><td>0.234</td></tr><tr><td>Albumin, n, %<sup>b</sup></td><td>42 (41.2)</td><td>11 (32.4)</td><td>0.420</td></tr><tr><td>Prealbumin, n, %<sup>b</sup></td><td>68 (66.7)</td><td>23 (67.6)</td><td>&gt;0.99</td></tr><tr><td>Hepatitis B e antibody, n, %<sup>b</sup></td><td>61 (59.8)</td><td>22 (64.7)</td><td>0.687</td></tr></tbody></table>

<table><caption>**Table 1:** (continued)</caption><thead><tr><th rowspan="2">Characteristic</th><th colspan="2">In-house dataset</th><th rowspan="2">p-Value</th></tr><tr><th>Training</th><th>Testing</th></tr></thead><tbody><tr><td>Hepatitis B surface antigen, n, %<sup>b</sup></td><td>66 (64.7)</td><td>24 (70.6)</td><td>0.676</td></tr><tr><td>Hepatitis B e antigen, n, %<sup>b</sup></td><td>13 (12.7)</td><td>4 (11.8)</td><td>&gt;0.99</td></tr><tr><td>Hepatitis B surface antibody, n, %<sup>b</sup></td><td>21 (20.6)</td><td>9 (26.5)</td><td>0.481</td></tr><tr><td>Hepatitis B core antibody, n, %<sup>b</sup></td><td>99 (97.1)</td><td>34 (100.0)</td><td>0.573</td></tr><tr><td>D-dipolymer, n, %<sup>b</sup></td><td>36 (35.3)</td><td>12 (35.3)</td><td>&gt;0.99</td></tr><tr><td>Glutamic pyruvic transaminase, n, %<sup>b</sup></td><td>28 (27.5)</td><td>7 (20.6)</td><td>0.502</td></tr><tr><td>Glutamyltranspeptidase, n, %<sup>b</sup></td><td>69 (67.6)</td><td>25 (73.5)</td><td>0.669</td></tr><tr><td>Alkaline phosphatase, n, %<sup>b</sup></td><td>31 (30.4)</td><td>14 (41.2)</td><td>0.294</td></tr><tr><td>LR-TR, n, %</td><td></td><td></td><td>0.120</td></tr><tr><td>Nonviable</td><td>24 (23.5)</td><td>13 (38.2)</td><td></td></tr><tr><td>Viable</td><td>78 (76.5)</td><td>21 (61.8)</td><td></td></tr></tbody></table>

strategy was applied, using the “LR-TR viable” and “LR-TR nonviable” classes as the stratification variable. This maintained consistent class proportions across all folds and minimized potential bias from outcome imbalance. The external validation set (n=42) underwent the same standardization.

## Interpretable image feature extraction network

Initial extraction of radiomic features from lesion images across multiple MRI sequences was performed using the PyRadiomics algorithm. The extracted high-dimensional features were preliminarily transformed through a multi-layer perceptron (MLP) to enhance representation and reduce redundancy. The resulting multi-sequence feature vectors were then augmented with a learnable class token and processed through the Transformer encoder block for advanced feature extraction and prognostic classification. The multi-head attention mechanism within the Transformer was used to capture dependencies among different MRI sequences, while the resulting attention weights provided a quantitative basis for model interpretability.

In natural language processing (NLP) tasks, researchers first convert each word in a sentence into a specific vector and represent the semantics of the sentence in the form of a vector sequence. Following the approach in NLP, we employ the radiomics feature vector of each image sequence as the “word vector”, and consider all six radiomics feature vectors as a whole sentence to represent the original information of the patient (Figure 1B). The image information for each

<footnote><sup>a</sup>Represents the proportion of the phenomenon described by the baseline indicator being present, while <sup>b</sup>represents the proportion of this baseline indicator exceeding the normal range of values.</footnote>
patient can be represented as a set of radiomic feature vectors $I$, denoted as $\{I_{T1WI}, I_{T2WI}, I_{DWI}, I_{AP}, I_{PVP}, I_{DP}\}_{i}$.

We initially transform $I_*$ into a vector $I_*$ of dimension $d$ through a linear mapping, and then use the MLP$_k$ module (a feature extraction module comprising $k$ multi-layer perceptrons with residual connections, which maintain the feature dimension) with the Gaussian Error Linear Unit (GELU) activation function to convert $I_*$ into a feature vector $I_*^m$. At this stage, $I_*^m$ represents a refined feature vector that encapsulates higher-level information.

The Transformer neural network is capable of considering the interdependencies between word vectors, thereby capturing the contextual relationships within a sentence [27]. In order to leverage this characteristic of the Transformer, we treat each MLP-processed $I_*^m$ as a higher-level token, analogous to a word vector in natural language processing tasks. The six $I_*^m$ vectors from different image sequences, along with a CLS (Class Token) vector are concatenated into a matrix $T$, as detailed in Equation (1).

$$T = [\text{CLS}, I_{T1WI}^m, I_{T2WI}^m, I_{DWI}^m, I_{AP}^m, I_{PVB}^m, I_{DP}^m], T \in \mathbb{R}^{d \times 7} \quad (1)$$

where CLS is a learnable parameter vector that captures global features. Given that the absolute positions of the feature vectors corresponding to each sequence and the total length of tokens remain unchanged during training and prediction, we do not incorporate positional embeddings into $I_*^m$. Subsequently, the matrix $T$ is further processed for feature extraction using $m$ Transformer encoder block modules. Each encoder block comprises a multilayer perceptron (MLP) layer and a multi-head self-attention layer (w-head). The multi-head attention mechanism in the self-attention layer enables the model to learn contextual dependencies and complementary relationships among features derived from different MRI sequences, allowing for holistic integration of information across all images. It should be noted that this mechanism is conceptually analogous to attention modeling in language tasks, but here it captures inter-sequence contextual associations in imaging data rather than linguistic relationships. We extract the vector CLS' at the class token position from the $T$ matrix processed by the final encoder block. Subsequently, we apply a linear transformation and pass it through a sigmoid activation function to obtain the predicted probability $\hat{p}^{\text{MR}}$, which is formulated in Equation (2):

$$\hat{p}^{\text{MR}} = \text{sigmoid}(\beta^T \text{CLS}' + \beta), \quad (2)$$

where sigmoid($x$)=1/[1+exp($-x$)], $\beta$ represents the vector of linear regression coefficients, and $\beta_0$ denotes the bias term.

The loss function of the model is defined using a weighted cross-entropy loss, which is formulated in Equation (3):

$$\text{Loss} = -\frac{1}{n} \sum_{i=1}^{n} \left[ \frac{n_0}{n_1} p_i \log \hat{p}_i^{\text{MR}} + (1-p_i) \log (1-\hat{p}_i^{\text{MR}}) \right]. \quad (3)$$

Additionally, the attention scores of the CLS' vector within the final encoder block are extracted as interpretability indicators for the model. These scores serve to indicate the degree of reference given by the model to images from different time periods for a specific patient. The attention scores are obtained by averaging the attention weights from each head within the multi-head attention layer. The higher the attention score corresponding to an image sequence, the more important that sequence is for the model's judgment. Optimal model parameters were established as $k=3$, $d=128$, $w=3$, $m=1$ after five-fold cross-validation on the training set.

The mean attention weight for each imaging feature sequence was calculated by averaging the weights across the three heads of the Transformer encoder. This method effectively identifies the sequence types that receive the highest attention from the model for each sample, thereby elucidating the sequences that contribute most significantly to the decision-making process.

## Model training and implementation

Model development and analysis were performed using Python (v3.8.8) and PyTorch (v2.0.0 + cu117). The Adam optimizer was applied with an initial learning rate of 0.0001, using a binary cross-entropy loss function and a batch size of 32. Model training was conducted for 500 steps. Five-fold stratified cross-validation was used to ensure balanced representation of “LR-TR viable” and “LR-TR nonviable” samples across folds. All radiomic and clinical features were z-score normalized prior to model input. All experiments were performed on a workstation running Ubuntu 20.04, equipped with an NVIDIA A100 GPU (80 GB VRAM). The final model hyper parameters were selected based on the best validation AUC averaged across folds, and the robustness of model performance was confirmed through bootstrap resampling and external validation to ensure robustness and reproducibility.

## Postoperative activity prediction network integrating multi-sequence imaging features and clinical factors

To enhance the predictive accuracy of the postoperative activity model, we integrated the predictive probabilities derived from MRI images with selected clinical factors into a
logistic regression framework. The resulting model is formulated in Equation (4):

$$ \hat{p} = \text{sigmoid}(\beta^T x_c + \beta_{\text{MR}} \hat{p}_i^{\text{MR}} + \beta_0), \quad (4) $$

Where $\hat{p}$ represents the predicted probability from the logistic regression model, $\beta$ denotes the regression coefficient vector for the candidate modeling factors, $x_c$ is an 11-dimensional vector of candidate factors. Additionally, $\beta_{MR}$ is the regression coefficient for the image-based predictor, and $\beta_0$ is the intercept coefficient. It is essential to clarify that the regression coefficients $\beta$ and $\beta_0$ here are distinct from the regression coefficients in Equation (2).

## Statistical analysis

Statistical analyses were conducted using R software (v4.2.2). Continuous variables were summarized with means and SDs, while categorical variables were expressed as proportions. For comparisons between groups, the Mann-Whitney U test was used for continuous data, and Fisher's exact test was applied to categorical data [28–30]. Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values were generated and calculated using the “pROC” package (v1.18.5). Descriptive statistical tables were produced using the “tableone” package (v0.13.2). The neural network was developed using PyTorch (v2.0.0+cu117) in Python 3.8.8.

To ensure comprehensive model evaluation, 95 % confidence intervals (CIs) were calculated for all AUCs using the bootstrap method (1,000 resamples). Model calibration was assessed by plotting calibration curves comparing predicted and observed probabilities, and by applying the Hosmer-Lemeshow goodness-of-fit test. A non-significant p-value (p>0.05) indicated good calibration. Decision curve analysis (DCA) was performed to quantify the clinical net benefit of the model across a range of threshold probabilities (0–1), comparing it against two reference strategies: “intervene for all” and “intervene for none.” A nomogram was built by the R package “regplot” as a quantitative tool for clinicians for individualized prediction. A p-value <0.05 was considered statistically significant.

## Results

### Overview of patient participants

Among patients from SUANFPH, 86.0 % were male and 14.0 % were female, with a mean age of 64.0 years (standard deviation [SD] = 10.7) (Table 1). Of those, 41.9 % were classified as BCLC stage A, 29.4 % as B, 21.3 % as C, and the remaining 7.4 %

were stage 0. In addition, 27.2 % of the patients were identified as LR-TR unviable post-surgery. For the patients recruited from ATHNU, 76.2 % were male and 23.8 % were female, with a mean age of 62.9 years (SD, 10.3) (Supplementary Table S3). 73.8 % of patients were in BCLC stage A, 19.1 % in stage B, and 7.1 % in stage C. Furthermore, 23.8 % of the patients were deemed LR-TR nonviable after TACE.

### Superiority of multi-sequence MRI

The postoperative activity prediction model leveraging multi-sequence MRI images demonstrated significant predictive prowess, evidenced by an AUC of 0.90 (95 % CI: 0.79–1.00) in the internal testing set (Figure 2A). In the external validation set, the model maintained a stable performance with an AUC of 0.79 (95 % CI: 0.62–0.95), affirming its efficacy across varying sampling institutions and instrumentation (Figure 2B).

Further insights into the model's decision-making were gleaned through analysis of attention weights derived from its attention mechanism (Figure 2D). Predominantly, the arterial, DWI, T2WI, and PVP were identified as receiving the highest overall attention, among which T2WI was the most prominent. This indicates their critical role in the model's assessments of postoperative activity. Intriguingly, a direct correlation was observed between increased attention to DWI and higher prediction probabilities, highlighting its association with positive outcomes in patient diagnoses.

While the T1WI and DP garnered less attention overall, they exhibited high attention levels in specific cases, underscoring their potential relevance for postoperative activity prediction in particular diagnostic contexts. To explore the superiority of modeling with all six-sequence images retained, we sequentially excluded each sequence of images and conducted separate modeling for postoperative activity. We observed that the absence of any one sequence of images led to a decline in the model's predictive performance. Notably, the exclusion of the DWI, T2WI, and PVP led to the most significant performance decrements, with AUCs dropping to 0.81. These findings reinforce the integral predictive value of all six sequences, especially DWI, T2WI, and PVP, which consistently carry higher attention weights across patient evaluations (Figure 2C).

Importantly, the multi-head attention mechanism also enhanced model interpretability by quantifying the contribution of each MRI sequence to final predictions. The higher attention weights assigned to DWI and T2WI reflect the model's reliance on biologically and clinically meaningful information – tissue cellularity, water diffusion, and intratumoral heterogeneity – that are well-recognized indicators of treatment response. This alignment between learned
<figure><img src="image_4.png" /><figcaption>**Figure 2:** Model performance and interpretability indicators. (A) ROC curves depicting the performance of the postoperative activity prediction model based on multi-sequence MRI images in both training and test sets. (B) ROC curve demonstrating the model's efficacy in external data. (C) Distribution of attention weights across different sequences for various patients, sorted by ascending prediction probability of the image-based model. The accompanying bar chart visualizes the overall attention weights for the six sequences across all evaluated patients. (D) Impact on model performance after exclusion of any one of the six sequences, with 'DL_All' representing the scenario where all sequences are retained. (E) ROC curves for the logistic regression model in both in-house and external validation datasets. (F) Distribution of nomogram AUC in internal and external datasets under bootstrap validation. (G) Comparison of predicted scores from the logistic model against actual postoperative activity in the external validation dataset. (H) Calibration curve for the model within the in-house dataset. (I) Calibration curve for the model within the external validation dataset. (J) Decision curve analysis illustrating the clinical utility of the logistic model ('logistic'), which integrates MRI images and clinical factors. 'All' represents the assumption that all patients exhibit viable tumors, while 'none' assumes all tumors are nonviable.</figcaption></figure>
attention and radiologic knowledge reinforces both the transparency and clinical plausibility of the proposed deep learning framework.

To further explore feature-level interpretability, we performed an exploratory correlation analysis within the T2WI sequence, which received the highest attention weights. We identified 10 radiomic features showing the strongest Spearman correlations with the model's predicted logits (|r|≈0.31–0.36; Table 2). These texture- and wavelet-derived descriptors reflect local intensity variations and heterogeneity within the tumor. The modest correlation values indicate that the model captures complex nonlinear interactions among multiple radiomic features rather than relying on a few dominant predictors – consistent with the representational behavior of deep-learning architectures.

To further demonstrate the advantage of our Transformer-based framework over conventional radiomics approaches, we conducted a head-to-head comparison with a LASSO regression model built using the same MRI-derived radiomic features (Supplementary Figure S1). The LASSO model achieved AUCs of 0.88 (95 % CI, 0.80–0.96) in the training set and 0.80 (95 % CI, 0.62–0.98) in the test set, which were inferior to those obtained by our deep learning model (AUCs: 1.00 and 0.90, respectively). These results highlight the superior generalization and discriminative performance of the proposed Transformer architecture compared with traditional feature-based radiomics modeling.

## Enhanced postoperative activity prediction model integrating imaging and clinical factors

To assess the contribution of clinical factors to postoperative activity predictions, we integrated 11 candidate modeling clinical factors with image-based prediction probabilities

into a logistic regression model (Table 1). Given that the image-based predictor alone achieved a highly fitting effect in the training set (Figure 2A), we combined the original training and internal testing sets, which represent the entire in-house dataset, to develop and train the logistic regression model, aiming to avoid overfitting and allow the model to comprehensively consider clinical factors. Inclusion of clinical variables significantly improved the model's predictive accuracy, with the AUC in the external validation set reaching 0.84 (95 % CI: 0.65–1.00) (Figure 2E). Of note, both the ALBI grade and the image-based prediction probability were identified as significant predictors of postoperative activity in the regression model (Table 3). Further validation through bootstrap resampling on the in-house and external datasets showed the AUC stabilizing at approximately 0.98 and 0.86, respectively (Figure 2F), indicating consistency in model performance. Moreover, the bootstrap test comparing the two ROC curves yielded a p-value of 0.160, indicating no significant difference in predictive performance between datasets and supporting that the model did not overfit despite the moderate sample size. These findings suggest that the sample size (n=178) was sufficient for model training and validation in this multicenter study. The logistic regression also tended to assign higher predictive scores (linear terms) to positive samples and lower scores to negative samples in the external validation, confirming the model's discriminative capacity (Figure 2G). Furthermore, calibration analysis demonstrated excellent agreement between predicted and observed probabilities, with non-significant Hosmer-Lemeshow p-values (0.315 in-house and 0.150 external) and calibration curves closely following the ideal diagonal (Figure 2H and I). DCA further showed that the integrated model provided greater clinical net benefit than the “treat-all” or “treat-none” strategies across most threshold probabilities, underscoring its potential clinical utility (Figure 2J).

<table><caption>**Table 2:** Top 10 T2WI radiomic features mostly correlated with model predictions.</caption><thead><tr><td>Feature name</td><td>Spearman's ρ</td></tr></thead><tbody><tr><td>wavelet.LH Schaum_Strength</td><td>0.358</td></tr><tr><td>wavelet.LH Schaum_Busyness</td><td>-0.347</td></tr><tr><td>wavelet.HH Schaum_Strength</td><td>0.338</td></tr><tr><td>lbp.2D_glszm_LargeAreaEmphasis</td><td>-0.324</td></tr><tr><td>lbp.2D_glszm_ZoneVariance</td><td>-0.323</td></tr><tr><td>lbp.2D_glszm_LargeAreaHighGrayLevelEmphasis</td><td>-0.322</td></tr><tr><td>lbp.2D_glszm_ZonePercentage</td><td>0.322</td></tr><tr><td>original_shape_Maximum2DdiameterSlice</td><td>-0.321</td></tr><tr><td>lbp.2D_glszm_LargeAreaLowGrayLevelEmphasis</td><td>-0.319</td></tr><tr><td>log.sigma.3.0.mm.3D_glszm_ZoneEntropy</td><td>-0.315</td></tr></tbody></table>

## Individualized postoperative activity prediction and clinical application

To enhance clinical utility, we developed a nomogram that incorporates clinical factors into a logistic regression model, facilitating individualized postoperative activity predictions (Table 4, Figure 3A). The effectiveness of this nomogram is demonstrated through detailed case studies of two representative patients – one with an LR-TR viable lesion and another with an LR-TR nonviable lesion. The LR-TR viable patient's lesion shows persistent enhancement during the AP post-TACE (indicated by a red arrow in Figure 3B) and washout changes in the PVP (green arrow in Figure 3B),
<table><caption>**Table 3:** Clinical factors showing significant differences between the LR-TR viable and the LR-TR nonviable groups.</caption><thead><tr><th>Clinical factors</th><th>Statistical tests</th><th>p-Value</th></tr></thead><tbody><tr><td>PVTT</td><td>Fisher's exact</td><td>0.004</td></tr><tr><td>T</td><td>Mann-Whitney U</td><td>&lt;0.001</td></tr><tr><td>Necrosis</td><td>Fisher's exact</td><td>0.020</td></tr><tr><td>Hemorrhage</td><td>Fisher's exact</td><td>0.010</td></tr><tr><td>Corona enhancement</td><td>Fisher's exact</td><td>0.002</td></tr><tr><td>Diameter of viable lesions</td><td>Mann-Whitney U</td><td>&lt;0.001</td></tr><tr><td>D-dimer</td><td>Fisher's exact</td><td>0.001</td></tr><tr><td>ALT</td><td>Fisher's exact</td><td>0.004</td></tr><tr><td>GGT</td><td>Fisher's exact</td><td>0.012</td></tr><tr><td>ALP</td><td>Fisher's exact</td><td>0.040</td></tr><tr><td>ALBI grade</td><td>Fisher's exact</td><td>&lt;0.001</td></tr></tbody></table>

PVTT, portal/hepatic vein invasion; T, tumor size; ALT, glutamic pyruvic transaminase; GGT, glutamyltranspeptidase; ALP, alkaline phosphatase; ALBI, albumin-bilirubin.

suggesting ongoing lesion viability. The nomogram predicts this patient's likelihood of being nonviable at 0.33 (Figure 3C). Conversely, the lesion of the LR-TR nonviable patient exhibits no significant enhancement during the AP and PVP post-TACE (Figure 3D), suggesting inactivity. The nomogram predicts the probability of this patient being nonviable at 0.832 (Figure 3E), aligning closely with the observed postoperative conditions of both patients.

Discussion

TACE is widely used for treating HCC, with the tumor's response being crucial for patient management and prognosis. Previous studies predicting early TACE treatment response have mostly used machine learning approaches combining radiomics with clinical risk factors [15–17, 31, 32]. To our knowledge, this is the first study combining MRI radiomics with clinical risk factors using a DL model for this purpose. The Transformer framework of our DL model treats different MRI sequences as an interconnected whole and considers contextual relationships among different MRI sequences. Unlike traditional methods focusing on image patches [33], our model processes sequences of features extracted by pyradiomics, capturing multidimensional lesion information using a fixed extraction protocol [34]. This approach enhances training performance and prevents overfitting, which is particularly valuable in studies with smaller sample sizes. Additionally, the attention mechanism of Transformer provides interpretable insights into the importance of different sequences through attention weights. We found that radiomic features alone could predict post-TACE activity, and integrating clinical

<table><caption>**Table 4:** Coefficient of logistic regression model integrating multi-sequence MRI image model prediction probabilities and clinical factors</caption><thead><tr><th>Factors</th><th>Coefficient</th><th>p-Value</th></tr></thead><tbody><tr><td>Intercept</td><td>-0.735</td><td>0.105</td></tr><tr><td>PVTT</td><td>-0.361</td><td>0.333</td></tr><tr><td>T</td><td>-0.484</td><td>0.157</td></tr><tr><td>Necrosis</td><td>-0.097</td><td>0.779</td></tr><tr><td>Hemorrhage</td><td>-0.079</td><td>0.821</td></tr><tr><td>Corona enhancement</td><td>-0.358</td><td>0.329</td></tr><tr><td>Diameter of viable lesions</td><td>-0.293</td><td>0.323</td></tr><tr><td>D-dimer</td><td>-0.417</td><td>0.226</td></tr><tr><td>ALT</td><td>-0.363</td><td>0.319</td></tr><tr><td>GGT</td><td>-0.107</td><td>0.749</td></tr><tr><td>ALP</td><td>0.079</td><td>0.827</td></tr><tr><td>ALBI grade</td><td>-0.658</td><td>0.045<sup>a</sup></td></tr><tr><td>MR probability</td><td>1.714</td><td>&lt;0.001<sup>a</sup></td></tr></tbody></table>

factors further enhanced individualized pre-surgery predictions.

One of the primary objectives of this study was to investigate the advantages of joint modeling using multi-sequence MRI images. Previous studies have shown that multi-sequence MRI can capture more comprehensive multidimensional information of lesions and avoid the limitations of single-sequence information. Meanwhile, multi-sequence complementarity can improve model robustness and reduce single-sequence bias or noise interference [35, 36]. To achieve this, we constructed our model based on images from six MRI sequences. Our findings demonstrated that the model achieved the highest AUC value of 0.90 when incorporating data from all six sequences. Notably, the exclusion of the T2WI sequence resulted in a significant decline in model performance, with the AUC dropping from 0.90 to 0.81, representing one of the most substantial reductions observed among all sequences. This observation aligns with previous studies, which have highlighted the superior capability of T2WI in capturing lesion-specific features. The T2WI sequence primarily reflects differences in the transverse relaxation of tissues, thereby enhancing the contrast between lesions and surrounding tissues, which facilitates more accurate lesion identification. These results underscore the critical role of T2WI in modeling cancer-related data [15, 37]. Furthermore, as a standard non-enhanced imaging sequence, T2WI offers practical advantages over contrast-enhanced imaging, making it a promising candidate for future research applications.

<footnote><sup>a</sup>Indicates that the regression coefficient of this variable is significantly different from 0, with p-values <0.05. Intercept refers to the intercept term of the model. PVTT, portal/hepatic vein invasion; T, tumor size; ALT, glutamic pyruvic transaminase; GGT, glutamyltranspeptidase; ALP, alkaline phosphatase; ALBI, albumin-bilirubin.</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 3:** The nomogram of the logistic regression model. (A) Nomogram used in the logistic regression model for predicting postoperative outcomes. (B) MRI images of an LR-TR viable patient (ID 20024). (C) Predicted results for an LR-TR viable patient in the nomogram. (D) MRI images of an LR-TR nonviable patient (ID 20010). (E) Predicted results for an LR-TR nonviable patient in the nomogram.</figcaption></figure>

The prominent contribution of T2WI in our model likely reflects its biological sensitivity to microstructural changes within hepatocellular carcinoma. As a sequence sensitive to tissue water content, T2WI can capture variations related to tumor cellularity, edema, necrosis, and fibrosis, which are dynamically altered following embolization. Viable tumor regions typically exhibit higher cellular density and restricted extracellular space, resulting in relatively lower and more homogeneous T2 signal intensity, whereas necrotic or fibrotic areas display increased water content and heterogeneous T2 signal patterns [38]. Consequently, texture features extracted from T2WI may serve as quantitative surrogates for intratumoral heterogeneity and treatment-induced tissue remodeling [39]. This biological interpretability reinforces the clinical relevance of T2WI as a dominant contributor in our attention-weighted model.

Assessing liver reserve function is crucial in HCC management, as cirrhosis often affects mortality [40]. This function assumes greater significance in early-stage HCC compared to more advanced stages [41, 42]. Traditionally, the Child-Turcotte-Pugh (CTP) classification evaluates liver reserve but includes subjective assessments and doesn't account for the 10–20 % of HCC patients without cirrhosis [5, 43, 44]. Therefore, we used the ALBI grade, based on objective laboratory values, offering an unbiased liver function

assessment with demonstrated prognostic capability [45]. Previous studies have leveraged ALBI in predicting survival and refining treatment strategies [46, 47]. Our study also confirms a significant correlation between ALBI grade and treatment response, supporting its utility in predicting HCC outcomes.

Notably, Alpha-fetoprotein (AFP) levels did not predict tumor response post-TACE in our study, aligning with evidence suggesting that a high baseline level of AFP (>20 ng/mL) may not relate to the initial treatment response [48]. However, decreased AFP levels post-TACE have been linked to improved survival [49, 50]. Previous studies found a significant correlation between tumor size and treatment response [51, 52]. Similarly, we observed a marginal association between tumor size and postoperative activity.

Intriguingly, intratumoral necrosis showed a negative regression coefficient but did not reach statistical significance. Although previous studies have linked intratumoral necrosis to poorer overall and recurrence-free survival in HCC [53], this feature often reflects impaired local perfusion and vascular abnormality within the tumor microenvironment [54]. Because TACE induces ischemic necrosis by blocking the arterial blood supply, lesions with preexisting necrotic regions may be more susceptible to further ischemic injury and treatment-induced non-viability.
Therefore, the observed trend toward a negative association
may represent a biologically plausible relationship rather
than a purely statistical artifact. Nonetheless, given the
limited sample size, this potential association should be
interpreted cautiously and validated in larger prospective
cohorts.

Currently, clinical judgment of TACE retreatment timing mostly relies on imaging follow-up 1–3 months after surgery, which has a certain lag. This tool can quickly predict tumor activity in the early postoperative period. If the prediction indicates that the tumor remains viable and the patient’s liver function is tolerable, retreatment can be initiated promptly to prevent tumor progression. If the prediction shows tumor non-viability, the follow-up interval can be extended to reduce unnecessary interventional procedures, lowering medical costs and the risk of patient complications. Crucially, the LR-TR nomogram was developed to guide individualized patient decisions about additional treatment. Traditional probability predictions may not effectively capture clinical implications. We conducted a decision curve analysis to assess whether the nomogram enhances patient outcomes. This analysis focuses on threshold probabilities to determine net benefit, considering the balance between true positives and false positives. Results show that using the LR-TR nomogram improves clinical outcomes compared to treat-all or treat-none approaches, even at low threshold probabilities. This finding highlights the nomogram’s potential to optimize patient management based on individual risk assessments.

From a translational perspective, the proposed model is
computationally efficient and can be implemented on stan-
dard GPU-equipped workstations. In a clinical workflow,
radiomic features could be automatically extracted from
manually or AI-annotated tumor regions across multi-
sequence MRI scans, followed by Transformer-based prob-
ability estimation and nomogram-assisted integration with
clinical factors. Such a pipeline could be readily incorpo-
rated into existing hospital information systems, enabling
real-time and reproducible assessment of early TACE
response in routine practice.

Several limitations merit attention. First, the retro-
spective design and stringent inclusion and exclusion
criteria resulted in a moderate overall sample size,
including 42 cases in the external validation cohort, despite
multicenter participation. These criteria were necessary to
ensure high-quality imaging and complete clinical data
across institutions. To mitigate potential limitations in
generalizability, we performed bootstrap resampling on
both in-house and external datasets, which demonstrated
stable AUCs and no significant performance differences
between cohorts, supporting the robustness of our findings.
Moreover, inclusion of patients representing a broad range

of BCLC stages enhances the study's relevance to real-world
clinical practice. Second, MRI images were acquired using
five different scanners with varying parameters, and three
contrast agents were applied, which may introduce bias in
the assessment of texture features. Nevertheless, this di-
versity contributes to developing more robust, generaliz-
able models that require further validation. Additionally,
while biopsy is the gold standard for assessing tumor
viability, it is often impractical, especially in China's
healthcare system. Therefore, we relied on assessments
using the LR-TR algorithm, DSA, and follow-up outcomes to
evaluate tumor activity effectively.

Conclusions

Our study demonstrates that integrating pre-TACE MRI texture features with clinical risk factors using deep learning can accurately predict early postoperative efficacy in HCC. This approach enables clinicians to devise tailored and more effective treatment strategies for HCC patients, potentially improving therapeutic outcomes.

**Acknowledgement:** We would like to thank all the patients and families who consented to participate in this study.

**Research ethics:** Ethics approval was obtained from the Ethics Committee of Nantong First People's Hospital (Approval No. 2023KT174) and the Ethics Committee of Nantong Tumor Hospital (Approval No. 2022-067). The study adheres to the ethical standards of the Declaration of Helsinki.

**Informed consent:** The requirement for informed consent was waived because this study was a retrospective analysis of existing medical records, involved no interventions, and all patient data were anonymized and handled confidentially in accordance with institutional privacy protocols. The research team had no direct contact with patients during the study period.

**Author contributions:** Conceptualization, Xiaole Fan, Bosheng He and Feng Feng; Methodology, Xiaole Fan, Yuhang Wang, Xu Sun, Loïc Cabannes, and Trung Hieu Ngo; Software, Xiaole Fan and Yuhang Wang; Formal analysis, Yuhang Wang, and Xu Sun; Statistical analysis: Yuhang Wang and Fangrong Yan; Visualization: Yuhang Wang; Investigation, all authors; Data interpretation: Xiaole Fan, Yuhang Wang, Jushun Yang, Jianan Cai, Ming Zhang, Tianle Wang, Li Zhu, Meihong Sheng, Bosheng He, and Feng Feng; Writing—original draft, all authors; Funding acquisition, Xiaole Fan, Bosheng He, Jushun Yang, and Feng Feng; Supervision, Bosheng He and Feng Feng.
**Use of Large Language Models, AI and Machine Learning**
**Tools:** None declared.

**Conflicts of Interest:** The authors declare no conflicts of interest to report regarding the present study. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

**Research funding:** This work was supported by the Nantong Municipal Health Commission Scientific Research Project (No. QNZ2023027) (Xiaole Fan), the Ministry of Education Collaborative Education Program (No. 231000669311915) (Bosheng He), the Provincial Health Commission Key Scientific Research Project of Jiangsu (No. ZD2021059) (Bosheng He), the Jiangsu Provincial Health Commission Scientific Research Project (No. K2023013) (Bosheng He), the Nantong Municipal Health Commission Scientific Research Project (No. MS20230276) (Jushun Yang), and the Nantong Municipal Health Commission Scientific Research Project (No. MS2022051) (Feng Feng).

**Data Availability:** The data that support the findings of this study are available from the Corresponding Author, Bosheng He, upon reasonable request.

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