MRI-based radiomic nomogram for predicting disease-free survival in patients with locally advanced rectal cancer

作者：Jun Liu, Ke Liu, Fang Cao, Pingsheng Hu, Feng Bi, Siye Liu, Lian Jian, Jumei Zhou, Shaolin Nie, Qiang Lu, Xiaoping Yu, Lu Wen · 发表于：Abdominal Radiology · 年份：2024 · DOI：10.1007/s00261-024-04710-0 · 被引用次数：8 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Colorectal Cancer Surgical Treatments、Colorectal and Anal Carcinomas

PURPOSE: Individual prognosis assessment is of paramount importance for treatment decision-making and active surveillance in cancer patients. We aimed to propose a radiomic model based on pre- and post-therapy MRI features for predicting disease-free survival (DFS) in locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (nCRT) and subsequent surgical resection. METHODS: This retrospective study included a total of 126 LARC patients, which were randomly assigned to a training set (n = 84) and a validation set (n = 42). All patients underwent pre- and post-nCRT MRI scans. Radiomic features were extracted from higher resolution T2-weighted images. Pearson correlation analysis and ANOVA or Relief were utilized for identifying radiomic features associated with DFS. Pre-treatment, post-treatment, and delta radscores were constructed by machine learning algorithms. An individualized nomogram was developed based on significant radscores and clinical variables using multivariate Cox regression analysis. Predictive performance was evaluated by the C-index, calibration curve, and decision curve analysis. RESULTS: The results demonstrated that in the validation set, the clinical model including pre-surgery carcinoembryonic antigen (CEA), chemotherapy after radiotherapy, and pathological stage yielded a C-index of 0.755 (95% confidence interval [CI]: 0.739-0.771). While the optimal pre-, post-, and delta-radscores achieved C-indices of 0.724 (95%CI: 0.701-0.747), ...