2020
DOI: 10.3389/fonc.2020.564307
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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 … Show more

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Cited by 18 publications
(10 citation statements)
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References 32 publications
(37 reference statements)
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“…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.…”
Section: Discussionsupporting
confidence: 86%
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“…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.…”
Section: Discussionsupporting
confidence: 86%
“…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 ).…”
Section: Discussionmentioning
confidence: 99%
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“…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 ].…”
Section: Discussionmentioning
confidence: 99%
“…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 ].…”
Section: Diagnosis Of Hcc By Ct/mri Based Radiomicsmentioning
confidence: 99%