Quantitative radiomics and qualitative LI-RADS imaging descriptors for non-invasive assessment of β-catenin mutation status in hepatocellular carcinoma.

Purpose: Gain-of-function mutations in CTNNB1, gene encoding for β-catenin, are observed in 25-30% of hepatocellular carcinomas (HCCs). Recent studies have shown β-catenin activation to have distinct roles in HCC susceptibility to mTOR inhibitors and resistance to immunotherapy. Our goal was to develop and test a computational imaging-based model to non-invasively assess β-catenin activation in HCC, since liver biopsies are often not done due to risk of complications.

Methods: This IRB-approved retrospective study included 134 subjects with pathologically proven HCC and available β-catenin activation status, who also had either CT or MR imaging of the liver performed within 1 year of histological assessment. For qualitative descriptors, experienced radiologists assessed the presence of imaging features listed in LI-RADS v2018. For quantitative analysis, a single biopsy proven tumor underwent a 3D segmentation and radiomics features were extracted. We developed prediction models to assess the β-catenin activation in HCC using both qualitative and quantitative descriptors.

Results: There were 41 cases (31%) with β-catenin mutation and 93 cases (69%) without. The model's AUC was 0.70 (95% CI 0.60, 0.79) using radiomics features and 0.64 (0.52, 0.74; p = 0.468) using qualitative descriptors. However, when combined, the AUC increased to 0.88 (0.80, 0.92; p = 0.009). Among the LI-RADS descriptors, the presence of a nodule-in-nodule showed a significant association with β-catenin mutations (p = 0.015). Additionally, 88 radiomics features exhibited a significant association (p < 0.05) with β-catenin mutations.

Conclusion: Combination of LI-RADS descriptors and CT/MRI-derived radiomics determine β-catenin activation status in HCC with high confidence, making precision medicine a possibility.