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

Abstract

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.

Radiomics Based on Contrast-Enhanced MRI in Differentiation Between Fat-Poor Angiomyolipoma and Hepatocellular Carcinoma in Noncirrhotic Liver: A Multicenter Analysis

“…Due to the rarity of hepatic AML, especially the cases with no or minimal fat, most of these studies enrolled a small number of patients. A study with a relatively large sample size of 30 hepatic epithelioid AML indicated that specific MRI features, such as intra-tumor vessel, draining hepatic vein, prolonged enhancement, and lack of capsule, may contribute to a more confident diagnosis, consistent with the results of some previous reports ( 7 , 27 ). However, some authors have put forward different views.…”

“…As far as we know, there is only one study based on MRI radiomics to distinguish hepatic AML from HCC. Recently, Liang et al ( 7 ) demonstrated that the radiomics model based on AP images performed well in distinguishing epithelioid AML from HCC and focal nodular hyperplasia, especially for MRI. This was similar to our results; however, the study had not been externally verified, and the performance of the model in other participant data was not clear ( 26 ).…”

“…However, the amount of fat component in the hepatic AML varies greatly, ranging from 10 to 90% of the tumor volume ( 4 ) and, in some instances, cannot be easily identified on imaging ( 5 , 6 ). In that case, many radiologists tend to misdiagnose these fat-poor AMLs (fp-AMLs) as other common hypervascular liver tumors, particularly hepatocellular carcinoma (HCC), with a frequency of 50% due to the overlapping imaging features ( 7 ), especially in areas with a high prevalence of hepatic viral infections like China. This can lead to unsuitable therapeutic schemes such as surgical therapy and liver transplantation.…”

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

“…An MRI-based study in 369 patients with 446 lesions (HCC 222, hemangioma 224) reported an AUC of 0.89 (sensitivity 0.822, specificity 0.714) for differentiating between HCC and hemangioma using images with in-phase, out-phase, T2-weighted, and diffusion-weighted imaging sequences [ 184 ]. In addition, according to a more recent study with both CT and MRI, fusion models that simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT) and 0.971 (MRI), with 10-foldcross-validation to differentiate hepatic epithelioid angiomyolipoma from HCC and FNH [ 185 ]. Furthermore, a multicenter retrospective cohort study performed in 178 cirrhosis patients (with indeterminate liver nodules including other malignant lesions as cholangiocarcinoma and metastasis, regenerative nodule, hemangioma and FNH) reported an AUC of 0.66 to diagnose HCC using triphasic contrast-enhanced CT, and suggested the benefit of AI to enhance clinicians’ decisions by identifying a subgroup of patients with high HCC risk [ 169 ].…”

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

“…This AUC was significantly higher than that of the less experienced radiologist (2 years of experience) (sensitivity 0.625, specificity 0.779; AUC 0.702, p < 0.05); however, it showed no significant difference from the experienced radiologist (10 years of experience) (sensitivity 0.915, specificity 0.901; AUC 0.908, p > 0.05) [ 21 ]. In addition, according to a more recent study with both CT and MRI, fusion models that simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT) and 0.971 (MRI), with 10-fold cross-validation to differentiate hepatic epithelioid angiomyolipoma from HCC and FNH [ 22 ]. Furthermore, a multicenter retrospective cohort study performed in 178 cirrhosis patients (with indeterminate liver nodules including other malignant lesions (cholangiocarcinoma <CC> and metastasis), regenerative nodule, hemangioma and FNH) reported an AUC of 0.66 to diagnose HCC using triphasic contrast-enhanced CT, and suggested the benefit of AI to enhance clinicians’ decisions by identifying a subgroup of patients with high HCC risk [ 23 ].…”