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

• Published in: The Lancet. Digital health 2022-01, Vol.4 (1), p.e8-e17
• Main Authors: Feng, Lili, Liu, Zhenyu, Li, Chaofeng, Li, Zhenhui, Lou, Xiaoying, Shao, Lizhi, Wang, Yunlong, Huang, Yan, Chen, Haiyang, Pang, Xiaolin, Liu, Shuai, He, Fang, Zheng, Jian, Meng, Xiaochun, Xie, Peiyi, Yang, Guanyu, Ding, Yi, Wei, Mingbiao, Yun, Jingping, Hung, Mien-Chie, Zhou, Weihua, Wahl, Daniel R, Lan, Ping, Tian, Jie, Wan, Xiangbo
• Format: Article
• Language: English
• Subjects: Aged; Artificial Intelligence - standards; Female; Humans; Male; Middle Aged; Neoadjuvant Therapy - methods; Prospective Studies; Rectal Neoplasms - therapy; Retrospective Studies
• ISSN: 2589-7500; 2589-7500
• DOI: 10.1016/S2589-7500(21)00215-6

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. 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, positive predictive value (PPV), and negative predictive value (NPV). Between Sept 25, 2009, and Nov 3, 2017, 303 patients were retrospectively recruited in the training cohort, 480 in validation cohort 1, and 150 in validation cohort 2; 100 eligible patients were enrolled in the prospective study between Jan 10 and June 10, 2020. RAPIDS had favourable accuracy for the prediction of pathological complete response in the training cohort (AUC 0·868 [95% CI 0·825–0·912]), and in validation cohort 1 (0·860 [0·828–0·892]) and validation cohort 2 (0·872 [0·810–0·934]). In the prospective validation study, RAPIDS had an AUC of 0·812 (95% CI 0·717–0·907), sensitivity of 0·888 (0·728–0·999), specificity of 0·740 (0·593–0·886), NPV of 0·929 (0·862–0·995), and PPV of 0·512 (0·313–0·710). RAPIDS also significantly outperformed single-modality prediction models (AUC 0·630 [0·507–0·754] for the pathomics microenvironment model, 0·716 [0·580–0·852] for the radiomics MRI model, and 0·733 [0·620–0·845] for the pathomics nucl