Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models

作者：Wenjie Liang, Jiayuan Shao, Weihai Liu, Shijian Ruan, Wuwei Tian, Xiuming Zhang, Dalong Wan, Qiang Huang, Yong Ding, Wenbo Xiao · 发表于：Frontiers in Oncology · 年份：2020 · DOI：10.3389/fonc.2020.564307 · 被引用次数：26 · 研究领域：Renal cell carcinoma treatment、Radiomics and Machine Learning in Medical Imaging、Pancreatic and Hepatic Oncology Research

Background: We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH). Methods: Totally, preoperative contrast-enhanced computed tomography (CT) data of 170 patients and preoperative contrast-enhanced magnetic resonance imaging (MRI) data of 137 patients were enrolled in this study. Quantitative texture features and wavelet features were extracted from the regions of interest (ROIs) of each patient imaging data. Then two radiomics signatures were constructed based on CT and MRI radiomics features respectively using the random forest (RF) algorithm. By integrating radiomics signatures with clinical characteristics, two radiomics-based fusion models were established through multivariate linear regression and 10-fold cross-validation. Finally, two diagnostic nomograms were built to facilitate the clinical application of the fusion models. Results: The radiomics signatures based on the RF algorithm achieved the optimal predictive performance with both CT and MRI data. The area under the receiver operating characteristic curves (AUCs) reached 0.996, 0.879, 0.999 and 0.925 for the training as well as test cohort from CT and MRI data respectively. Then, two fusion models simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT data) and 0.971 (MRI data) with 10-fold cross-validation. Through dec...