Attention-guided framework for integrative omics and temporal dynamics in predicting major pathological response in neoadjuvant immunochemotherapy for NSCLC

作者：Xiangfeng Gan, Jianzhong He, Wei Zhang, Wenzeng Chen, Shijiancong Liu, Wenhao Li, Xiaohui Duan, Liangzhan Lv, Yi Liang, Qingdong Cao, Baishen Chen · 发表于：Journal for ImmunoTherapy of Cancer · 年份：2025 · DOI：10.1136/jitc-2025-012526 · 被引用次数：11 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Cancer Immunotherapy and Biomarkers

OBJECTIVE: This study developed a multiomics model combining radiomics, pathomics, and temporal imaging to predict major pathological response in patients with locally advanced non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy. METHODS: A retrospective, multicenter study was conducted, enrolling 271 patients with stage IB-III NSCLC who received neoadjuvant immunochemotherapy. High-resolution CT images were enhanced using a generative adversarial network-based super-resolution technique. Radiomics features were extracted from multi-sequence CT scans at multiple time points, while pathomics features were derived from whole-slide imaging of surgical specimens. A transformer-based attention mechanism was used to integrate radiomics, pathomics, and temporal imaging data. The model was trained and validated on data from one center and tested on external cohorts. Performance was evaluated using area under the curve (AUC), net reclassification improvement, integrated discrimination improvement, and decision curve analysis. RESULTS: The Trans-Model demonstrated superior predictive performance, achieving an AUC of 0.858 (95% CI 0.783 to 0.933) in the external test cohort. It outperformed Rad-Model (AUC: 0.839) and Patho-Model (AUC: 0.753). The Trans-Model effectively stratified patients by survival outcomes, with major pathological response (MPR)-positive patients exhibiting significantly improved 3-year overall survival (87.3% vs 76.1%, p=0.034) and 5-year p...