Abstract 2782: Automated segmentation of hepatocellular carcinoma lesions on contrast-enhanced MRI using an AI model in patients with cirrhosis.

Abstract Purpose: Develop an automated hepatocellular carcinoma (HCC) detection model on multi-phasic contrast-enhanced T1 MRI images. Methods: Multi-phase (pre-contrast, arterial, venous, and delayed phases) contrast-enhanced T1 MRIs were obtained from two institutions (Center 1: n=106; Center 2: n=87) and acquired between 2007 and 2023. An expert radiologist manually contoured 1794 focal liver lesions across 586 scans and assigned each lesion a score using the Liver Imaging Reporting and Data System (LI-RADS). Six 3D full-resolution nnU-Net models were trained on multi-phase (pre-contrast, arterial, venous, and delayed) or only arterial-phase contrast-enhanced T1 images. Lesion size (maximum lesion diameter ≥ 1 cm) and lesion score (LI-RADS ≥ 3) criteria were applied to assess the impact on the training of these nnU-Net models. Model performance was evaluated at a scan and lesion level. Performance metrics included dice similarity coefficient (DSC), sensitivity, specificity, accuracy, and positive predictive value (PPV). Results: The arterial phase without lesion criteria was determined to have the best overall performance among the models developed. At the scan level, the arterial phase model achieved 93% (40/43) sensitivity, 46.6% (7/15) specificity, and 81% (47/58) accuracy. At a lesion level for the arterial phase model, the performance dropped slightly, with 65.9% (126/191) sensitivity, 68.1% (126/185) PPV, and 51.8% (133/257) accuracy. In a failure analysis, it was observed that 45.2% (57/126) of true-positive lesions had a LIRADS score ≥ 4, while 9.2% (6/65) of false-negative lesions had a LIRADS score ≥ 4. Conclusion: An arterial phase contrast-enhanced MRI nnU-Net model was developed on multi-institutional data to detect HCC in patients with cirrhosis, producing promising results. This study indicates that AI models can improve detection in high-risk patients. Citation Format: Emma J. Stevenson, Nathan Lay, Stephanie A. Harmon, Haoyue Zhang, Fahmida Haque, Peter Choyke, Theo Heller, Ross Filice, Baris Turkbey, Christine Hsu. Automated segmentation of hepatocellular carcinoma lesions on contrast-enhanced MRI using an AI model in patients with cirrhosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2782.