Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study

作者：Lili Feng, Zhenyu Liu, Chaofeng Li, Zhenhui Li, Xiaoying Lou, Lizhi Shao, Yunlong Wang, Yan Huang, Haiyang Chen, Xiaolin Pang, Shuai Liu, Fang He, Jian Zheng, Xiaochun Meng, Peiyi Xie, Guanyu Yang, Yi Ding, Mingbiao Wei, Jing‐Ping Yun, Mien‐Chie Hung, Weihua Zhou, Daniel Wahl, Ping Lan, Jie Tian, Xiang‐Bo Wan · 发表于：The Lancet Digital Health · 年份：2021 · DOI：10.1016/s2589-7500(21)00215-6 · 被引用次数：334 · 研究领域：Colorectal Cancer Surgical Treatments、Radiomics and Machine Learning in Medical Imaging、Colorectal and Anal Carcinomas

BACKGROUND: Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides. METHODS: In this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, po...