Feasibility of magnetic resonance imaging-based radiomics features for preoperative prediction of extrahepatic cholangiocarcinoma stage

Aim: The aim of this study is to develop and test radiomics models based on magnetic resonance imaging (MRI) to preoperatively and respectively predict the T stage, perineural invasion, and microvascular invasion of extrahepatic cholangiocarcinoma (eCCA) through a non-invasive approach. Methods: This research included 101 eCCA patients (29-83 years; 45 females and 56 males) between August 2011 and December 2019. Radiomics features were retrospectively extracted from T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient map using MaZda software. The region of interest was manually delineated in the largest section on four MRI images as ground truth while keeping 1-2 mm margin to tumor border, respectively. Pretreatment, dimension reduction method, and classifiers were used to establish radiomics signatures for assessing three pathological characteristics of eCCA. Finally, independent training and testing datasets were used to assess radiomics signature performance based on receiver operating characteristic curve analysis, accuracy, precision, sensitivity, and specificity. Results: This study extracted 1208 radiomics features from four MRI images of each patient. The best performing radiomics signatures for assessing the T stage, perineural invasion, and microvascular invasion were respectively produced by L1_normalization + linear discriminant analysis (LDA) + logistic regression, Box_Cox transformer + LDA +K-nearest neighbor, and L2_normalization + LDA + AdaBoost. The area under the curve values of the radiomics signatures for predicting the training and testing cohorts in each subgroup were respectively 1 and 0.962 (T stage), 1 and 1 (both perineural invasion and microvascular invasion). Conclusion: These proposed radiomic models based on MR images had powerful performance and high potential in predicting T stage, perineural, and microvascular invasion of eCCA. Reporting guidelines/Research design: Prognostic study. (C) 2021 Elsevier Ltd. All rights reserved.