Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancer
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 know...
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| Published in | Scientific data Vol. 12; no. 1; pp. 768 - 8 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
10.05.2025
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2052-4463 2052-4463 |
| DOI | 10.1038/s41597-025-05125-2 |
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| 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. |
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| AbstractList | 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. 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. 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.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. |
| ArticleNumber | 768 |
| Author | Wu, Min Du, Jinchao Liu, Li Zhao, Ling Luo, Jiawei Huang, Shixin Wan, Xiaoyu Nie, Xixi Peng, Xin |
| Author_xml | – sequence: 1 givenname: Jiawei surname: Luo fullname: Luo, Jiawei organization: West China Biomedical Big Data Center, West China Hospital; Med-X Center for Informatics, Sichuan University – sequence: 2 givenname: Xiaoyu surname: Wan fullname: Wan, Xiaoyu organization: School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications – sequence: 3 givenname: Jinchao surname: Du fullname: Du, Jinchao organization: Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine – sequence: 4 givenname: Li surname: Liu fullname: Liu, Li organization: Department of Radiology, The People’s Hospital of Yubei District of Chongqing city – sequence: 5 givenname: Ling surname: Zhao fullname: Zhao, Ling organization: Department of Radiology, The People’s Hospital of Yubei District of Chongqing city – sequence: 6 givenname: Xin surname: Peng fullname: Peng, Xin organization: Department of Radiology, The People’s Hospital of Yubei District of Chongqing city – sequence: 7 givenname: Min surname: Wu fullname: Wu, Min organization: College of Biomedical Engineering, Chongqing Medical University – sequence: 8 givenname: Shixin surname: Huang fullname: Huang, Shixin email: d200101011@stu.cqupt.edu.cn organization: Department of Scientific Research, The People’s Hospital of Yubei District of Chongqing city – sequence: 9 givenname: Xixi surname: Nie fullname: Nie, Xixi email: niexx@cqupt.edu.cn organization: School of Computer Science and Technology, Chongqing University of Posts and Telecommunications |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40348741$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.jhep.2022.08.021 10.1007/s10916-023-01968-7 10.1186/s12885-024-12334-2 10.1016/j.neunet.2023.06.013 10.1186/s40644-024-00686-8 10.57760/sciencedb.12207 10.1002/lt.23897 10.1007/s00521-023-08957-4 10.1186/s13045-021-01167-2 10.1109/SIPROCESS.2019.8868690 10.1186/s40644-017-0110-z 10.1038/s41598-023-46580-4 10.1016/j.media.2022.102680 10.3390/cancers15072140 10.1145/3234150 10.1007/s11901-018-0431-9 10.3390/cancers15153880 10.1186/s12885-015-2026-y 10.1007/s11548-022-02653-9 10.1159/000509424 10.1186/s13244-023-01576-6 |
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| Snippet | Primary liver cancer is a significant global health issue with high incidence and mortality rates worldwide. Accurate diagnosis and classification of its... Abstract Primary liver cancer is a significant global health issue with high incidence and mortality rates worldwide. Accurate diagnosis and classification of... |
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| SubjectTerms | 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 |
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| Title | Comprehensive multi-phase 3D contrast-enhanced CT imaging for primary liver cancer |
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