Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/45761
Title: Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy
Authors: Zhang, Zhen
Luo, Tianchen
Yan, Meng
Shen, Haixia
Tao, Kaiyi
Zeng, Jian
Yuan, Jingping
Fang, Min
Zheng, Jian
BERMEJO DELGADO, Inigo 
Dekker, Andre
Ruysscher, Dirk De
Wee, Leonard
Zhang, Wencheng
Jiang, Youhua
Ji, Yongling
Issue Date: 2025
Publisher: BMJ PUBLISHING GROUP
Source: Journal for immunotherapy of cancer, 13 (3) (Art N° e011149)
Abstract: Background Accurate prediction of pathologic complete response (pCR) following neoadjuvant immunotherapy combined with chemotherapy (nICT) is crucial for tailoring patient care in esophageal squamous cell carcinoma (ESCC). This study aimed to develop and validate a deep learning model using a novel voxel-level radiomics approach to predict pCR based on preoperative CT images.Methods In this multicenter, retrospective study, 741 patients with ESCC who underwent nICT followed by radical esophagectomy were enrolled from three institutions. Patients from one center were divided into a training set (469 patients) and an internal validation set (118 patients) while the data from the other two centers was used as external validation sets (120 and 34 patients, respectively). The deep learning model, Vision-Mamba, integrated voxel-level radiomics feature maps and CT images for pCR prediction. Additionally, other commonly used deep learning models, including 3D-ResNet and Vision Transformer, as well as traditional radiomics methods, were developed for comparison. Model performance was evaluated using accuracy, area under the curve (AUC), sensitivity, specificity, and prognostic stratification capabilities. The SHapley Additive exPlanations analysis was employed to interpret the model's predictions.Results The Vision-Mamba model demonstrated robust predictive performance in the training set (accuracy: 0.89, AUC: 0.91, sensitivity: 0.82, specificity: 0.92) and validation sets (accuracy: 0.83-0.91, AUC: 0.83-0.92, sensitivity: 0.73-0.94, specificity: 0.84-1.0). The model outperformed other deep learning models and traditional radiomics methods. The model's ability to stratify patients into high and low-risk groups was validated, showing superior prognostic stratification compared with traditional methods. SHAP provided quantitative and visual model interpretation.Conclusions We present a voxel-level radiomics-based deep learning model to predict pCR to neoadjuvant immunotherapy combined with chemotherapy based on pretreatment diagnostic CT images with high accuracy and robustness. This model could provide a promising tool for individualized management of patients with ESCC.
Notes: Jiang, YH; Ji, YL (corresponding author), Chinese Acad Sci, Zhejiang Canc Hosp, Hangzhou Inst Med HIM, Hangzhou, Zhejiang, Peoples R China.; Ji, YL (corresponding author), Zhejiang Key Lab Prevent Diag & Therapy Gastrointe, Hangzhou, Zhejiang, Peoples R China.
zhen.zhang@maastro.nl; luotianchen218@163.com; yan_meng_1999@126.com;
c39329@163.com; taoky@zjcc.org.cn; 68640770@qq.com;
yuanjingping@whu.edu.cn; fangmin@zjcc.org.cn; zhengJian@tmu.edu.cn;
inigo.bermejo@uhasselt.be; andre.dekker@maastro.nl;
dirk.deruysscher@maastro.nl; leonard.wee@maastro.nl; wczhang@tmu.edu.cn;
jiangyh@zjcc.org.cn; jiyl@zjcc.org.cn
Keywords: Immunotherapy;Esophageal Cancer;Neoadjuvant;Chemotherapy
Document URI: http://hdl.handle.net/1942/45761
e-ISSN: 2051-1426
DOI: 10.1136/jitc-2024-011149
ISI #: 001445657500001
Rights: Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group. s This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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