OpenSwissHCC: a multiparametric liver MRI dataset with multiple tumor and liver segmentations of pre-treatment hepatocellular carcinomas

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and a leading cause of cancer-related mortality worldwide. While dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a key role for HCC screening, diagnosis and treatment planning, the development of AI models for detection and characterization of focal liver lesions is hindered by the scarcity of publicly available annotated MRI datasets. Here, we present OpenSwissHCC, a curated multiphasic dataset of 132 multiparametric DCE-MRI scans from adult patients with chronic liver disease acquired on 1.5T and 3T MRI scanners between 2011 and 2020. The dataset includes both HCC-positive (n=63) and HCC-negative (n=69) patients, with up to 5 lesions per patient segmented across each sequence when confidently identified. Whole-liver and lesion segmentations were manually performed and expert-validated, with lesion metadata including LI-RADS scores size liver segment location major LI-RADS imaging features (non-rim APHE, washout, capsule, threshold growth) key ancillary/background findings (e.g., mosaic architecture, fat/blood products, necrosis, tumor-in-vein; cirrhosis and portal-hypertension signs) In total, 140 lesions were annotated, including 97 confirmed HCC. Additionally, we provide liver masks for all T1-weighted images (WI) sequences, generated using a pre-trained nnU-Net model, as well as pre-computed transforms to facilitate registration across T1-WI phases. By providing a comprehensive, well-annotated, and standardized collection of liver MRI examinations, OpenSwissHCC aims to assist the development and benchmarking of advanced computational methods for automated HCC detection, segmentation, and characterization, fostering progress toward precision imaging and personalized treatment planning.