A clinical‐radiomics model incorporating T2‐weighted and diffusion‐weighted magnetic resonance images predicts the existence of lymphovascular invasion / perineural invasion in patients with colorectal cancer

作者：Ke Zhang, Yiyue Ren, Shufeng Xu, Wei Lu, Shengnan Xie, Jiali Qu, Xiaoyan Wang, Bo Shen, Peipei Pang, Xiujun Cai, Jihong Sun · 发表于：Medical Physics · 年份：2021 · DOI：10.1002/mp.15001 · 被引用次数：27 · 研究领域：Colorectal Cancer Surgical Treatments、Radiomics and Machine Learning in Medical Imaging、Hepatocellular Carcinoma Treatment and Prognosis

PURPOSE: Lymphovascular invasion (LVI) and perineural invasion (PNI) are independent prognostic factors in patients with colorectal cancer (CRC). In this study, we aimed to develop and validate a preoperative predictive model based on high-throughput radiomic features and clinical factors for accurate prediction of LVI/PNI in these patients. METHODS: Two hundred and sixty-three patients who underwent colorectal resection for histologically confirmed CRC between 1 February 2011 and 30 June 2020 were retrospectively enrolled. Between 1 February 2011 and 30 September 2018, 213 patients were randomly divided into a training cohort (n = 149) and a validation cohort (n = 64) by a ratio of 7:3. We used a 10000-iteration bootstrap analysis to estimate the prediction error and confidence interval for two cohorts. The independent test cohort consisted of 50 patients between 1 October 2018 and 30 June 2020. Regions of interest (ROIs) were manually delineated in high-resolution T2-weighted and diffusion-weighted images using ITK-SNAP software on each CRC tumor slice. In total, 3356 radiomic features were extracted from each ROI. Next, we used the maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms to select the strongest of these features to establish a clinical-radiomics model for predicting LVI/PNI. Receiver-operating characteristic and calibration curves were then plotted to evaluate the predictive performance of the model in the trainin...