Dataset Information

Focal Nodular Hyperplasia and Hepatocellular Adenoma around the World Viewed through the Scope of the Immunopathological Classification.

ABSTRACT: Focal nodular hyperplasia (FNH) and hepatocellular adenoma (HCA) are benign hepatocellular tumors. The risk of bleeding and malignant transformation of HCA are strong arguments to differentiate HCA from FNH. Despite great progress that has been made in the differential radiological diagnosis of the 2 types of nodules, liver biopsy is sometimes necessary to separate the 2 entities. Identification of HCA subtypes using immunohistochemical techniques, namely, HNF1A-inactivated HCA (35-40%), inflammatory HCA (IHCA), and beta-catenin-mutated inflammatory HCA (b-IHCA) (50-55%), beta-catenin-activated HCA (5-10%), and unclassified HCA (10%) has greatly improved the diagnostic accuracy of benign hepatocellular nodules. If HCA malignant transformation occurs in all HCA subgroups, the risk is by far the highest in the ? -catenin-mutated subgroups (b-HCA, b-IHCA). In the coming decade the management of HCA will be more dependent on the identification of HCA subtypes, particularly for smaller nodules (<5?cm) in terms of imaging, follow-up, and resection.

SUBMITTER: Balabaud C

PROVIDER: S-EPMC3654480 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Focal Nodular Hyperplasia and Hepatocellular Adenoma around the World Viewed through the Scope of the Immunopathological Classification.

Balabaud Charles C Al-Rabih Wesal R WR Chen Pei-Jer PJ Evason Kimberley K Ferrell Linda L Hernandez-Prera Juan C JC Huang Shiu-Feng SF Longerich Thomas T Park Young Nyun YN Quaglia Alberto A Schirmacher Peter P Sempoux Christine C Thung Swan N SN Torbenson Michael M Wee Aileen A Yeh Matthew M MM Yeh Shiou-Hwei SH Le Bail Brigitte B Zucman-Rossi Jessica J Bioulac-Sage Paulette P

International journal of hepatology 20130414

Focal nodular hyperplasia (FNH) and hepatocellular adenoma (HCA) are benign hepatocellular tumors. The risk of bleeding and malignant transformation of HCA are strong arguments to differentiate HCA from FNH. Despite great progress that has been made in the differential radiological diagnosis of the 2 types of nodules, liver biopsy is sometimes necessary to separate the 2 entities. Identification of HCA subtypes using immunohistochemical techniques, namely, HNF1A-inactivated HCA (35-40%), inflamm ...[more]

PMID: 23691331

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Project description:BackgroundThe typical enhancement patterns of hepatocellular carcinoma (HCC) on contrast-enhanced ultrasound (CEUS) are hyper-enhanced in the arterial phase and washed out during the portal venous and late phases. However, atypical variations make a differential diagnosis both challenging and crucial. We aimed to investigate whether machine learning-based ultrasonic signatures derived from CEUS images could improve the diagnostic performance in differentiating focal nodular hyperplasia (FNH) and atypical hepatocellular carcinoma (aHCC).Patients and methodsA total of 226 focal liver lesions, including 107 aHCC and 119 FNH lesions, examined by CEUS were reviewed retrospectively. For machine learning-based ultrasomics, 3,132 features were extracted from the images of the baseline, arterial, and portal phases. An ultrasomics signature was generated by a machine learning model. The predictive model was constructed using the support vector machine method trained with the following groups: ultrasomics features, radiologist's score, and combination of ultrasomics features and radiologist's score. The diagnostic performance was explored using the area under the receiver operating characteristic curve (AUC).ResultsA total of 14 ultrasomics features were chosen to build an ultrasomics model, and they presented good performance in differentiating FNH and aHCC with an AUC of 0.86 (95% confidence interval [CI]: 0.80, 0.89), a sensitivity of 76.6% (95% CI: 67.5%, 84.3%), and a specificity of 80.5% (95% CI: 70.6%, 85.9%). The model trained with a combination of ultrasomics features and the radiologist's score achieved a significantly higher AUC (0.93, 95% CI: 0.89, 0.96) than that trained with the radiologist's score (AUC: 0.84, 95% CI: 0.79, 0.89, P < 0.001). For the sub-group of HCC with normal AFP value, the model trained with a combination of ultrasomics features, and the radiologist's score remain achieved the highest AUC of 0.92 (95% CI: 0.87, 0.96) compared to that with the ultrasomics features (AUC: 0.86, 95% CI: 0.74, 0.89, P < 0.001) and radiologist's score (AUC: 0.86, 95% CI: 0.79, 0.91, P < 0.001).ConclusionsMachine learning-based ultrasomics performs as well as the staff radiologist in predicting the differential diagnosis of FNH and aHCC. Incorporating an ultrasomics signature into the radiologist's score improves the diagnostic performance in differentiating FNH and aHCC.

Dataset Information

Focal Nodular Hyperplasia and Hepatocellular Adenoma around the World Viewed through the Scope of the Immunopathological Classification.

Publications

Focal Nodular Hyperplasia and Hepatocellular Adenoma around the World Viewed through the Scope of the Immunopathological Classification.

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