| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Computer Science, Artificial Intelligence |
| Manuscript Type |
Retrospective Study |
| Article Title |
Radiomics and deep learning predict neoadjuvant immunochemotherapy response in locally advanced esophageal squamous cell carcinoma
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
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 and Qi Wang |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| the Qingdao Postdoctoral Sustentation Fund |
RZ2100001380 |
|
| Corresponding Author |
Qi Wang, Associate Chief Physician, Department of Radiation Oncology, Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Shinan District, Qingdao, 266000, Shandong, China, Qingdao 266000, Shandong Province, China. qdfy_wq@qdu.edu.cn |
| Key Words |
Esophageal squamous cell carcinoma; Neoadjuvant immunochemotherapy; Radiomics; Deep learning; Treatment response prediction |
| 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. |
| Publish Date |
2026-10-20 03:52 |
| Citation |
Zhao BX, Zhang M, Fu WQ, Li XY, Han ML, Zhang J, Gao W, Guo TH, Li HY, Ai SW, Wang HJ, Zhang BY, Wang Q. Radiomics and deep learning predict neoadjuvant immunochemotherapy response in locally advanced esophageal squamous cell carcinoma. World J Gastroenterol 2026; 32(46): 124166
|
| URL |
https://www.wjgnet.com/1007-9327/full/v32/i46/124166.htm |
| DOI |
https://doi.org/10.3748/wjg.124166 |
