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World J Gastroenterol. Dec 14, 2026; 32(46): 124166
Published online Dec 14, 2026. doi: 10.3748/wjg.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, 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
Revised: July 15, 2026
Accepted: August 14, 2026
Published online: December 14, 2026
Processing time: 134 Days and 13.1 Hours
Core Tip
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.