Deep radiomics-based fusion model for prediction of bevacizumab treatment response and outcome in patients with colorectal cancer liver metastases: a multicentre cohort study

作者：Shizhao Zhou, Dazhen Sun, Wujian Mao, Yu Liu, Wei Cen, Lechi Ye, Fei Liang, Jianmin Xu, Hongcheng Shi, Yuan Ji, Lisheng Wang, Wenju Chang · 发表于：EClinicalMedicine · 年份：2023 · DOI：10.1016/j.eclinm.2023.102271 · 被引用次数：54 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Colorectal Cancer Treatments and Studies、Hepatocellular Carcinoma Treatment and Prognosis

Background Accurate tumour response prediction to targeted therapy allows for personalised conversion therapy for patients with unresectable colorectal cancer liver metastases (CRLM). In this study, we aimed to develop and validate a multi-modal deep learning model to predict the efficacy of bevacizumab in patients with initially unresectable CRLM using baseline PET/CT, clinical data, and colonoscopy biopsy specimens. Methods In this multicentre cohort study, we retrospectively collected data of 307 patients with CRLM from the BECOME study (NCT01972490) (Zhongshan Hospital of Fudan University, Shanghai) and two independent Chinese cohorts (internal validation cohort from January 1, 2018 to December 31, 2018 at Zhongshan Hospital of Fudan University; external validation cohort from January 1, 2020 to December 31, 2020 at Zhongshan Hospital—Xiamen, Shanghai, and the First Hospital of Wenzhou Medical University, Wenzhou). The main inclusion criteria were that patients with CRLM had pre-treatment PET/CT images as well as colonoscopy specimens. After extracting PET/CT features with deep neural networks (DNN) and selecting related clinical factors using LASSO analysis, a random forest classifier was built as the Deep Radiomics Bevacizumab efficacy predicting model (DERBY). Furthermore, by combining histopathological biomarkers into DERBY, we established DERBY + . The performance of model was evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive v...