Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancer
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05125-2 |
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| author | Jiawei Luo Xiaoyu Wan Jinchao Du Li Liu Ling Zhao Xin Peng Min Wu Shixin Huang Xixi Nie |
| author_facet | Jiawei Luo Xiaoyu Wan Jinchao Du Li Liu Ling Zhao Xin Peng Min Wu Shixin Huang Xixi Nie |
| author_sort | Jiawei Luo |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-4cbcc5439b5a4a03b2e4893b24c0c019 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-4cbcc5439b5a4a03b2e4893b24c0c0192025-08-20T03:09:34ZengNature PortfolioScientific Data2052-44632025-05-011211810.1038/s41597-025-05125-2Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancerJiawei Luo0Xiaoyu Wan1Jinchao Du2Li Liu3Ling Zhao4Xin Peng5Min Wu6Shixin Huang7Xixi Nie8West China Biomedical Big Data Center, West China Hospital; Med-X Center for Informatics, Sichuan UniversitySchool of Communications and Information Engineering, Chongqing University of Posts and TelecommunicationsDepartment of Radiology, Chongqing Hospital of Traditional Chinese MedicineDepartment of Radiology, The People’s Hospital of Yubei District of Chongqing cityDepartment of Radiology, The People’s Hospital of Yubei District of Chongqing cityDepartment of Radiology, The People’s Hospital of Yubei District of Chongqing cityCollege of Biomedical Engineering, Chongqing Medical UniversityDepartment of Scientific Research, The People’s Hospital of Yubei District of Chongqing citySchool of Computer Science and Technology, Chongqing University of Posts and TelecommunicationsAbstract 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.https://doi.org/10.1038/s41597-025-05125-2 |
| spellingShingle | Jiawei Luo Xiaoyu Wan Jinchao Du Li Liu Ling Zhao Xin Peng Min Wu Shixin Huang Xixi Nie Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancer Scientific Data |
| title | Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancer |
| title_full | Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancer |
| title_fullStr | Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancer |
| title_full_unstemmed | Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancer |
| title_short | Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancer |
| title_sort | comprehensive multi phase 3d contrast enhanced ct imaging for primary liver cancer |
| url | https://doi.org/10.1038/s41597-025-05125-2 |
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