NeoPred: dual-phase CT AI forecasts pathologic response to neoadjuvant chemo-immunotherapy in NSCLC

作者：Jianqi Zheng, Zeping Yan, Runchen Wang, Houlu Xiao, Zhenlin Chen, Xiaomin Ge, Zhigang Li, Zhichao Liu, Hong Yu, Hongxu Liu, Guan Wang, Pingwen Yu, Junke Fu, Guangjian Zhang, Jia Zhang, Bohao Liu, Ying Huang, Hongshen Deng, Chudong Wang, Wenhai Fu, Yuan Zhang, Rui Wang, Yu Jiang, Yuechun Lin, Linchong Huang, Chao Yang, Fei Cui, Jianxing He, Hengrui Liang · 发表于：Journal for ImmunoTherapy of Cancer · 年份：2025 · DOI：10.1136/jitc-2025-011773 · 被引用次数：11 · 研究领域：Lung Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Cancer Immunotherapy and Biomarkers

BACKGROUND: Accurate preoperative prediction of major pathological response or pathological complete response after neoadjuvant chemo-immunotherapy remains a critical unmet need in resectable non-small-cell lung cancer (NSCLC). Conventional size-based imaging criteria offer limited reliability, while biopsy confirmation is available only post-surgery. METHODS: We retrospectively assembled 509 consecutive NSCLC cases from four Chinese thoracic-oncology centers (March 2018 to March 2023) and prospectively enrolled 50 additional patients. Three 3-dimensional convolutional neural networks (pre-treatment CT, pre-surgical CT, dual-phase CT) were developed; the best-performing dual-phase model (NeoPred) optionally integrated clinical variables. Model performance was measured by area under the receiver-operating-characteristic curve (AUC) and compared with nine board-certified radiologists. RESULTS: In an external validation set (n=59), NeoPred achieved an AUC of 0.772 (95% CI: 0.650 to 0.895), sensitivity 0.591, specificity 0.733, and accuracy 0.627; incorporating clinical data increased the AUC to 0.787. In a prospective cohort (n=50), NeoPred reached an AUC of 0.760 (95% CI: 0.628 to 0.891), surpassing the experts' mean AUC of 0.720 (95% CI: 0.574 to 0.865). Model assistance raised the pooled expert AUC to 0.829 (95% CI: 0.707 to 0.951) and accuracy to 0.820. Marked performance persisted within radiological stable-disease subgroups (external AUC 0.742, 95% CI: 0.468 to 1.000; pros...