Luo Jiawei, Wan Xiaoyu, Du Jinchao, Liu Li, Zhao Ling, Peng Xin, Wu Min, Huang Shixin, Nie Xixi
West China Biomedical Big Data Center, West China Hospital; Med-X Center for Informatics, Sichuan University, Chengdu, 610044, China.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Sci Data. 2025 May 10;12(1):768. 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.
原发性肝癌是一个重大的全球健康问题，在全球范围内发病率和死亡率都很高。对其亚型进行准确诊断和分类对于选择正确的治疗方案和改善患者预后至关重要。对比增强计算机断层扫描（CECT）在诊断肝癌方面具有高灵敏度和特异性。然而，公开可用的肝癌CECT扫描数据集有限，且往往不能完全涵盖所有亚型，也未包括完整的CT扫描期相。我们假设使用具有完整扫描期相的三维CECT图像有助于开发和验证原发性肝癌的诊断及分割模型。因此，我们创建了一个带有肝脏和病变区域标注的CECT数据集。该数据集包括278例肝癌病例，涵盖肝细胞癌、肝内胆管癌以及肝细胞 - 胆管癌合并症，同时还有来自83名非肝癌受试者的CECT图像。它包含超过50000层肝癌病变图像。我们相信这个数据集能够为开发和验证原发性肝癌分类及分割模型提供有价值的支持。
