Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy

作者：Zhen Zhang, Tianchen Luo, Yan Meng, Haixia Shen, Kaiyi Tao, Jian Zeng, Jingping Yuan, Min Fang, Jianming Zheng, Iñigo Bermejo, André Dekker, Dirk De Ruysscher, Leonard Wee, Wencheng Zhang, Youhua Jiang, Yongling Ji · 发表于：Journal for ImmunoTherapy of Cancer · 年份：2025 · DOI：10.1136/jitc-2024-011149 · 被引用次数：44 · 研究领域：Esophageal Cancer Research and Treatment、Radiomics and Machine Learning in Medical Imaging、Cancer Immunotherapy and Biomarkers

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 (accur...