Predicting the microvascular invasion and tumor grading of intrahepatic mass-forming cholangiocarcinoma based on magnetic resonance imaging radiomics and morphological features

作者：Shuang Chen, Yumeng Zhu, Lijuan Wan, Shuangmei Zou, Hongmei Zhang · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2023 · DOI：10.21037/qims-23-11 · 被引用次数：16 · 研究领域：Cholangiocarcinoma and Gallbladder Cancer Studies、Gallbladder and Bile Duct Disorders、Radiomics and Machine Learning in Medical Imaging

Background: Preoperative diagnosis of microvascular invasion (MVI) and tumor grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) using imaging findings can facilitate patient treatment decision-making. This study was conducted to establish and validate nomograms based on magnetic resonance imaging (MRI) radiomics and morphological features for predicting the MVI and tumor grading of IMCC before radical hepatectomy. Methods: A total of 235 patients with resected IMCC at the Chinese Academy of Medical Sciences and Peking Union Medical College were divided into a training set (n=167) and a validation set (n=68), retrospectively. Clinical data and MRI morphological features were recorded. Univariate and multivariate analyses were conducted to identify the significant features for the prediction of MVI and tumor grading. Radiomics features were extracted from T2-weighted imaging fat-suppressed and diffusion-weighted imaging (DWI). Radiomics signatures (rad_scores) were built based on the least absolute shrinkage and selection operator (LASSO) method. Then, the nomograms were constructed by combining the rad_scores and the significant clinical or MRI morphologic features. The predictive performances for MVI and tumor grading were evaluated by the area under the receiver operating characteristic curve (AUC), calibration, and clinical utility. Results: Totals of 16 and 9 radiomics features were selected to build the rad_scores for the prediction of MVI and tumor grading fo...