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

Frontiers in Oncology

Wuwei Tian¹  Shijian Ruan¹  Yong Ding¹  Dalong Wan²  Xiuming Zhang³  Wenjie Liang⁴  Qiang Huang⁴  Wenbo Xiao⁴  Weihai Liu⁵  Jiayuan Shao⁶ 

[1] College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China;Department of Hepatobiliary and Pancreatic Surgery, College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, China;Department of Pathology, College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, China;Department of Radiology, College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, China;Department of Radiology, The People's Hospital of Beilun District, Ningbo, China;Polytechnic Institute, Zhejiang University, Hangzhou, China;

关键词: hepatic epithelioid angiomyolipoma;    hepatocellular carcinoma;    focal nodular hyperplasia;    radiomics;    machine learning;   

DOI  :  10.3389/fonc.2020.564307

来源: DOAJ

【 摘 要 】

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 in 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 decision curve analysis, the fusion models were proved to be excellent models to distinguish HEAML from HCC and FNH in comparison between the clinical models and radiomics signatures.Conclusions: Two radiomics-based models derived from CT and MRI images, respectively, performed well in distinguishing HEAML from HCC and FNH and might be potential diagnostic tools to formulate individualized treatment strategies.

【 授权许可】

Unknown