Abstract Liver resection is a cornerstone treatment for liver tumors, yet post-hepatectomy liver failure (PHLF) remains a severe and life-threatening complication with no effective treatment. Recent advances in artificial intelligence (AI) have shown promise in addressing this challenge; however, progress has been hindered by the limited availability of high-quality, expert-annotated, multi-center datasets. To bridge this critical gap, we present a multi-institutional dataset of preoperative Gd-EOB-DTPA-enhanced MRI scans, comprising 14,895 images from 220 patients across three academic medical centers. This comprehensive dataset includes 22,342 expert annotations of key anatomical structures (i.e., liver, Couinaud segments, liver tumors, spleen, and psoas muscle), detailed clinicopathological variables, and rigorously adjudicated PHLF outcomes. We also release U-Net-based automated segmentation tools to support reproducible region-of-interest delineation. Furthermore, we provide an illustrative (non-validated) pipeline demonstrating how FLR volumetry, MRI-derived functional parameters, and clinical risk factors can be integrated to support downstream analysis and methodological development. The described pipeline is illustrative and does not represent a validated predictive model. This dataset aims to facilitate research in FLR assessment and PHLF prediction, providing a resource for research on preoperative assessment.
Xie et al. (Wed,) studied this question.