Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III–IV d-MMR/MSI-H colorectal cancer

作者：Yiwen Zhang, Xuan Zhang, Xi Zhong, Liebin Huang, Wenjing Jiang, Caixia Zhang, Lizhu Liu, Ruimin You, Yanli Li, Xiaoping Yi, Zaiyi Liu, Xiaobo Chen, Zhenhui Li · 发表于：Journal for ImmunoTherapy of Cancer · 年份：2025 · DOI：10.1136/jitc-2025-011569 · 被引用次数：9 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Cancer Immunotherapy and Biomarkers、Colorectal Cancer Surgical Treatments

BACKGROUND: Neoadjuvant immunotherapy significantly improves the pathological complete response (pCR) rate in colorectal cancer (CRC). However, the lack of reliable tools to accurately identify responders remains a key barrier to its widespread clinical adoption. This study aimed to develop an interpretable radiomics model guided by immunophenotypes to predict response to preoperative immunotherapy in CRC, with the goal of enabling more precise and personalized treatment strategies. METHODS: First, we retrospectively collected 108 patients with CRC from the center who underwent preoperative CT and RNA sequencing. Immunophenotypes were characterized through unsupervised clustering of tumor-infiltrating immune cells at RNA level, with subsequent validation by CD3 and CD8 spatial distributions at immunohistochemical (IHC) level. Furthermore, patients from center II (n=19) and center III (n=22) receiving neoadjuvant immunotherapy were assigned to training and validation cohorts. Through a two-stage selection process, immunophenotype-associated radiomic features were initially identified, followed by immunotherapy response-related radiomics features that were further identified. Eventually, an interpretable immunotherapy response prediction model was developed by integrating a decision tree algorithm with SHapley Additive exPlanations (SHAP) analysis. RESULTS: Two immunophenotypes (immune-hot and immune-cold) were identified, compared with the immune-cold, the former exhibited mor...