Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancer

• Published in: Scientific data Vol. 12; no. 1; pp. 768 - 8
• Main Authors: Luo, Jiawei, Wan, Xiaoyu, Du, Jinchao, Liu, Li, Zhao, Ling, Peng, Xin, Wu, Min, Huang, Shixin, Nie, Xixi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.05.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/2321; 692/699/1503/1607/1610; Cancer; Carcinoma, Hepatocellular - diagnostic imaging; Cholangiocarcinoma; Cholangiocarcinoma - diagnostic imaging; Computed tomography; Contrast Media; Data Descriptor; Datasets; Hepatocellular carcinoma; Humanities and Social Sciences; Humans; Imaging, Three-Dimensional; Liver cancer; Liver Neoplasms - diagnostic imaging; multidisciplinary; Public health; Science; Science (multidisciplinary); Tomography, X-Ray Computed - methods
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-025-05125-2

Primary liver cancer is a significant global health issue with high incidence and mortality rates worldwide. Accurate diagnosis and classification of its subtypes are crucial for choosing the right treatment options and improving patient outcomes. Contrast-enhanced computed tomography (CECT) is known for its high sensitivity and specificity in diagnosing liver cancer. However, publicly available datasets of liver cancer CECT scans are limited and often do not fully cover all subtypes or include complete CT scan phases. We hypothesize that using 3D CECT images with complete scan phases can help develop and validate diagnostic and segmentation models for primary liver cancer. Therefore, we created a CECT dataset with annotated liver and lesion areas. This dataset includes 278 cases of liver cancer, featuring hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and combined hepatocellular-cholangiocarcinoma, along with CECT images from 83 non-liver cancer subjects. It contains over 50,000 layers of liver cancer lesion images. We believe this dataset can offer valuable support for developing and validating models for classifying and segmenting primary liver cancer.