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Objectives: Sonic hedgehog hepatocellular adenoma (shHCA) is a new hepatocellular adenoma (HCA) subgroup characterized by high risk of hemorrhage. ShHCA account for below 10% of all HCA cases and are often associated with female gender, obesity, and non-alcoholic steatohepatitis. No specific MRI characteristics have been described to date. The objective of this study was to assess the value of using MRI to identify shHCA, and correlate MRI findings with histology., Methods: We retrospectively collected MRI scans of 29 patients with shHCA from our center and from different liver referral centers to include 35 lesions. Diagnosis of shHCA was assessed by immunohistochemical overexpression of argininosuccinate synthase 1 or prostaglandin D2 synthase, then confirmed by molecular analysis of sonic hedgehog pathway activation and/or by proteomic analysis., Results: In 46% (n = 16/35) of shHCA cases, we detected intralesional fluid-filled cavities defined on MR images as fluid-like foci markedly hyperintense on T2-weighted sequences, and hypointense on T1-weighted sequences, with or without delayed enhancement. Pathologically, these cavities were observed in 54% of cases as vacuoles filled with blood at different stages of degradation. Hemorrhage and/or necrosis were detected among 71% of cases by MRI analysis (n = 25/35) versus 82% pathologically. Seventeen percent of shHCA cases (n = 6/35) were completely homogeneous via MRI and pathological analysis. No MRI criteria was found in favor of focal nodular hyperplasia, HNF1A-mutated HCA, or typical inflammatory HCA., Conclusion: We reveal the presence of intralesional fluid-filled cavities among 46% of our shHCA cases that represent a new MRI finding possibly helpful for shHCA diagnosis., Clinical Relevance Statement: This multicenter study is the first clinical study about the radiological aspect of this new hepatocellular adenoma subgroup. This highlights a strong correlation between MRI and histological analysis, with a specific pattern emerging for diagnosis., Key Points: • Sonic hedgehog hepatocellular adenoma is a new hepatocellular adenoma subgroup associated with high risk of hemorrhage, but imaging features of this subgroup remain unknown. • Analysis of MR images and correlation with pathology revealed intralesional fluid-filled cavities and necrotic-hemorrhagic changes. • Intralesional fluid-filled cavities have not yet been described in other adenoma subtypes and represent a new MRI finding for sonic hedgehog hepatocellular adenoma., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: Non-invasive subtyping of hepatocellular adenomas (HCA) remains challenging for several subtypes, thus carrying different levels of risks and management. The goal of this study is to devise a multivariable diagnostic model based on basic clinical features (age and sex) combined with MRI-radiomics and to evaluate its diagnostic performance., Methods: This single-center retrospective case-control study included all consecutive patients with HCA identified within the pathological database from our institution from January 2003 to April 2018 with MRI examination (T2, T1-no injection/injection-arterial-portal); volumes of interest were manually delineated in adenomas and 38 textural features were extracted (LIFEx, v5.10). Qualitative (i.e., visual on MRI) and automatic (computer-assisted) analysis were compared. The prognostic scores of a multivariable diagnostic model based on basic clinical features (age and sex) combined with MRI-radiomics (tumor volume and texture features) were assessed using a cross-validated Random Forest algorithm., Results: Via visual MR-analysis, HCA subgroups could be classified with balanced accuracies of 80.8 % (I-HCA or ß-I-HCA, the two being indistinguishable), 81.8 % (H-HCA) and 74.4 % (sh-HCA or ß-HCA also indistinguishable). Using a model including age, sex, volume and texture variables, HCA subgroups were predicted (multivariate classification) with an averaged balanced accuracy of 58.6 %, best=73.8 % (sh-HCA) and 71.9 % (ß-HCA). I-HCA and ß-I-HCA could be also distinguished (binary classification) with a balanced accuracy of 73 %., Conclusion: Multiple HCA subtyping could be improved using machine-learning algorithms including two clinical features, i.e., age and sex, combined with MRI-radiomics. Future HCA studies enrolling more patients will further test the validity of the model., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)