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World J Gastroenterol. Dec 14, 2026; 32(46): 124166
Published online Dec 14, 2026. doi: 10.3748/wjg.124166
Radiomics and deep learning predict neoadjuvant immunochemotherapy response in locally advanced esophageal squamous cell carcinoma
Bing-Xin Zhao, Meng Zhang, Wen-Qian Fu, Xin-Yu Li, Meng-Lin Han, Jing Zhang, Wen Gao, Tian-Hui Guo, Heng-Yan Li, Shi-Wen Ai, Hai-Ji Wang, Bi-Yuan Zhang, Qi Wang
Bing-Xin Zhao, Meng Zhang, Wen-Qian Fu, Xin-Yu Li, Meng-Lin Han, Wen Gao, Tian-Hui Guo, Hai-Ji Wang, Bi-Yuan Zhang, Qi Wang, Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
Jing Zhang, Department of Oncology, Affiliated Hospital of Jining Medical University, Jining 272000, Shandong Province, China
Heng-Yan Li, Department of Radiology, Affiliated Hospital of Jining Medical University, Jining 272000, Shandong Province, China
Shi-Wen Ai, Department of Thoracic Surgery, Affiliated Hospital of Jining Medical University, Jining 272000, Shandong Province, China
Author contributions: Zhao BX wrote the main manuscript text and prepared the methodology and visualization; Wang Q conceptualized the study, handled the software, and supervised the project; Li XY, Han ML, and Zhang J curated the data; Zhang M and Fu WQ performed the formal analysis; Gao W, Ai SW, and Li HY conducted the investigation; Wang HJ, Guo TH and Zhang BY reviewed and edited the manuscript; all authors critically reviewed and provided final approval of the manuscript; and all authors were responsible for the decision to submit the manuscript for publication.
AI contribution statement: AI tools (specifically ChatGPT) were used solely for linguistic refinement and formatting assistance. No AI tool was involved in the generation of research data, interpretation of results, or formulation of conclusions. All AI-generated outputs were critically reviewed and revised by the authors.
Supported by the Qingdao Postdoctoral Sustentation Fund, No. RZ2100001380.
Institutional review board statement: The study was approved by the Ethics Committee of the Affiliated Hospital of Qingdao University (Approval No. QYFY WZLL 30959) and the Research Ethics Committee of the Affiliated Hospital of Jining Medical University (Approval No. 2026-01-C021).
Informed consent statement: Informed consent was waived by the Research Ethics Committee of the Affiliated Hospital of Jining Medical University because of the retrospective nature of this study.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Corresponding author: Qi Wang, PhD, Associate Chief Physician, Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Shinan District, Qingdao 266000, Shandong Province, China.
qdfy_wq@qdu.edu.cn
Received: June 9, 2026
Revised: July 15, 2026
Accepted: August 14, 2026
Published online: December 14, 2026
Processing time: 134 Days and 13.1 Hours
BACKGROUND
Neoadjuvant immunochemotherapy (NICT) offers a promising treatment for locally advanced esophageal squamous cell carcinoma (ESCC), yet predicting which patients will achieve a pathological response remains challenging caused by the lack of reliable noninvasive biomarkers.
AIM
To construct and assess multimodal models integrating radiomics, deep learning (DL), and clinical data to predict NICT response in locally advanced ESCC.
METHODS
188 patients who received NICT and radical surgery were enrolled in this retrospective multicenter study. Clinical variables, radiomic features from contrast-enhanced computed tomography, and DL features from three pre-trained architectures (Densenet201, Resnet101, Vgg11) were collected. These features were used to build clinical, radiomics, radiomics-DL, and clinical-radiomics-DL models using four machine learning algorithms. End-to-end DL models were also developed. Model performance was evaluated using area under the curve (AUC), calibration, and decision curve analysis.
RESULTS
In the external validation cohort, single-modality models (clinical, radiomics, and DL) showed limited predictive performance. The radiomics-DL model constructed with RandomForest (RF) with Vgg11 features achieved the best overall performance, with AUCs of 0.998, 0.855, and 0.779 in the training, internal validation, and external validation cohorts, respectively, and demonstrated good calibration and consistent clinical net benefit. The clinical-radiomics-DL model did not further improve predictive performance.
CONCLUSION
The radiomics-DL model (Vgg11-RF) predicts NICT response in locally advanced ESCC with promising generalizability, offering a noninvasive tool for individualized treatment decisions.
Core Tip: Accurate prediction of response to neoadjuvant immunochemotherapy in locally advanced esophageal squamous cell carcinoma remains challenging. This multicenter study developed and compared clinical, radiomics, deep learning (DL), and multimodal models using pretreatment contrast-enhanced computed tomography images from 188 patients. The radiomics-DL model combining handcrafted radiomics features with Vgg11-derived DL features using RandomForest achieved the best performance, with areas under the curve of 0.998, 0.855, and 0.779 in training, internal validation, and external validation cohorts, respectively. This model demonstrated favorable generalizability across centers and offers a noninvasive tool for individualized treatment decision-making.
