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Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespan. Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. The recent popularization of artificial intelligence in computer-aid diagnosis creates opportunities for advances in areas such as (1) Computer-aided detection for locating suspect lesions such as mass and microcalcification, leaving the classification to the radiologist; and (2) Computer-aided diagnosis for characterizing the suspicious region of lesion and/or estimate its probability of onset; and (3) Findings of predictive image-based biomarkers by applying the computational methods to mine the potential relationships between image representation and molecular subtype, including luminal A, luminal B, HER2 positive, and Triple-negative. However, existing publicly available mammography databases are limited by small sample size, lack of diversity in patient populations, missing biopsy confirmations and unknown molecular sub-types. To help fill the gap, we built a database conducted on 1,775 patients from China with benign or malignant breast who underwent mammography examination between July 2012 and January 2016. The database consists of 3,728 mammographies from these 1,775 patients, with biopsy confirmed type of benign or malignant tumors. For 749 of these patients (1,498 mammographies) we also include patients' molecular subtypes. Image data were acquired on a GE Senographe DS mammography system. |
Acknowledgements
- The authors of this dataset thank the volunteers from the School of Computer Science and Engineering, South China University of Technology for assisting to tidy the clinical and imaging data. This work was supported by the grant from the National Natural Science Foundation of China (no.61771007)
Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
- Continue with any names from additional submitting sites if collection consists of more that one.
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active | true |
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title | Data Access |
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| Data AccessData Type | Download all or Query/Filter |
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Images, Segmentations, and Radiation Therapy Structures/Doses/Plans (DICOM, XX.X GB) << latter two items only if DICOM SEG/RTSTRUCT/RTDOSE/PLAN exist >> | (Download requires the NBIA Data Retriever) | Tissue Slide Images (SVS, XX.X GB) | | Clinical data (CSV) | | Genomics (web) | |
Click the Versions tab for more info about data releases. Please contact help@cancerimagingarchive.net with any questions regarding usage. |
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title | Detailed Description |
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| Detailed DescriptionImage Statistics |
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Modalities | MG | Number of Patients | 1775 | Number of Studies |
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| Number of Images | 3728 | Images Size (GB) |
<< Add any additional information as needed below. Likely would be something from site. >>- Mammography images were collected in .TIFF format.
- Clinical data are saved in .CSV format
- laterality and view in a CSV to facilitate our conversion to DICOM
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy Tcia license 4 international |
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| Cui, Chunyan; Li Li; Cai, Hongmin; Fan, Zhihao; Zhang, Ling; Dan, Tingting; Li, Jiao; Wang, Jinghua. (2020) The Chinese Mammography Database (CMMD): An online mammography database with biopsy confirmed types for machine diagnosis of breast. The Cancer Imaging Archive. DOI: DOI goes here. Create using Datacite with information from Collection Approval form |
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title | Publication Citation |
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| Cai, H. et al. Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms. Computational and Mathematical Methods in Medicine 2019, 2717454 (2019). Wang, J. et al. Discrimination of breast cancer with microcalcifications on mammography by deep learning. Scientific Reports 6, 27327–27327 (2016) We ask on the proposal form if they have ONE traditional publication they'd like users to cite. |
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| Only if they ask for special acknowledgments like funding sources, grant numbers, etc in their proposal. |
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| Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7 |
Other Publications Using This DataTCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. |
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| Version X (Current): Updated yyyy/mm/ddData Type | Download all or Query/Filter |
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Images (DICOM, xx.x GB) | | Clinical Data (CSV) | Link | Other (format) | |
<< One or two sentences about what you changed since last version. No note required for version 1. >>
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