Abstract
ObjectiveThis study aims to develop and externally validate a contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics-based model for preoperative differentiation between fat-poor angiomyolipoma (fp-AML) and hepatocellular carcinoma (HCC) in patients with noncirrhotic livers and to compare the diagnostic performance with that of two radiologists.MethodsThis retrospective study was performed with 165 patients with noncirrhotic livers from three medical centers. The dataset was divided into a training cohort (n = 99), a time-independent internal validation cohort (n = 24) from one center, and an external validation cohort (n = 42) from the remaining two centers. The volumes of interest were contoured on the arterial phase (AP) images and then registered to the venous phase (VP) and delayed phase (DP), and a total of 3,396 radiomics features were extracted from the three phases. After the joint mutual information maximization feature selection procedure, four radiomics logistic regression classifiers, including the AP model, VP model, DP model, and combined model, were built. The area under the receiver operating characteristic curve (AUC), diagnostic accuracy, sensitivity, and specificity of each radiomics model and those of two radiologists were evaluated and compared.ResultsThe AUCs of the combined model reached 0.789 (95%CI, 0.579–0.999) in the internal validation cohort and 0.730 (95%CI, 0.563–0.896) in the external validation cohort, higher than the AP model (AUCs, 0.711 and 0.638) and significantly higher than the VP model (AUCs, 0.594 and 0.610) and the DP model (AUCs, 0.547 and 0.538). The diagnostic accuracy, sensitivity, and specificity of the combined model were 0.708, 0.625, and 0.750 in the internal validation cohort and 0.619, 0.786, and 0.536 in the external validation cohort, respectively. The AUCs for the two radiologists were 0.656 and 0.594 in the internal validation cohort and 0.643 and 0.500 in the external validation cohort. The AUCs of the combined model surpassed those of the two radiologists and were significantly higher than that of the junior one in both validation cohorts.ConclusionsThe proposed radiomics model based on triple-phase CE-MRI images was proven to be useful for differentiating between fp-AML and HCC and yielded comparable or better performance than two radiologists in different centers, with different scanners and different scanning parameters.
Highlights
Hepatic angiomyolipoma (AML) is a mesenchymal benign tumor belonging to the perivascular epithelioid cell tumors (PEComas), which is a group of tumors believed to be derived from perivascular epithelioid cells and the co-expression of melanocytic and muscle marker
In view of the fact that AMLs are much less common than hepatocellular carcinoma (HCC), we randomly selected some of these patients according to the ratio of 1:2 to alleviate the offset caused by the distribution and improve the statistical power [20], relative to the number of AML patients who were eventually enrolled in each center, using a commercially available random number generator (QuickCalcs, GraphPad)
Male predominance was observed in the HCC group, while most fat-poor AMLs (fp-AMLs) patients were female [87% (48/55) vs. 18% (20/110), p < 0.001]
Summary
Hepatic angiomyolipoma (AML) is a mesenchymal benign tumor belonging to the perivascular epithelioid cell tumors (PEComas), which is a group of tumors believed to be derived from perivascular epithelioid cells and the co-expression of melanocytic and muscle marker. Many radiologists tend to misdiagnose these fat-poor AMLs (fp-AMLs) as other common hypervascular liver tumors, 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. It is crucial to accurately distinguish between fp-AML and HCC before surgery
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
