ArticleFrontiers in oncology2021
Radiomics Based on Contrast-Enhanced MRI in Differentiation Between Fat-Poor Angiomyolipoma and Hepatocellular Carcinoma in Noncirrhotic Liver: A Multicenter Analysis.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research progress of MRI-based radiomics in hepatocellular carcinoma.Frontiers in oncology · 2025Pooled it
- A T2 weighted imaging-based radiomics nomogram for the classification of hepatic blood-rich lesions: hepatocellular carcinoma and benign liver lesions.Discover oncology · 2025Article
- Artificial intelligence for detection and characterization of focal hepatic lesions: a review.Abdominal radiology (New York) · 2025Review
- MRI-based radiomics machine learning model to differentiate non-clear cell renal cell carcinoma from benign renal tumors.European journal of radiology open · 2024Article
- Review
- Focal Lesions of the Liver and Radiomics: What Do We Know?Diagnostics (Basel, Switzerland) · 2023Review
- Radiomics analysis of T1WI and T2WI magnetic resonance images to differentiate between IgG4-related ophthalmic disease and orbital MALT lymphoma.BMC ophthalmology · 2023Article
- Review
- Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses.IEEE open journal of engineering in medicine and biology · 2023Article
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Authors and funding
10 authors.
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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 (
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.
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