Summary
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).
- This work was partially supported by the Key-Area Research and Development of Guangdong Province under Grant (2020B010166002, 2020B1111190001), the National Natural Science Foundation of China (61472145, 61771007), Guangdong Natural Science Foundation (2017A030312008), and the Health & Medical Collaborative Innovation Project of Guangzhou City (201803010021, 202002020049).
Data Access
Data Type | Download all or Query/Filter |
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Images (DICOM, 22.9 GB) | (Download requires the NBIA Data Retriever) |
Clinical data (CSV) | |
Inclusion-Exclusion criteria (pdf) |
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Detailed Description
Image Statistics | |
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Modalities | MG |
Number of Patients | 1775 |
Number of Studies | 1775 |
Number of Series | 1775 |
Number of Images | 5202 |
Images Size (GB) | 22.9 GB |
- 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
Citations & Data Usage Policy
Data Citation
Cui, Chunyan; Li Li; Cai, Hongmin; Fan, Zhihao; Zhang, Ling; Dan, Tingting; Li, Jiao; Wang, Jinghua. (2021) The Chinese Mammography Database (CMMD): An online mammography database with biopsy confirmed types for machine diagnosis of breast. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/tcia.eqde-4b16
DOI IS IN DRAFT MODE WITHOUT AN ABSTRACT 3/12/21 -QUESTIONS:
Do we need to acknowledge DClunie's work to convert TIF to DICOM and how is that acknowledged,
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Publication Citation
Cai, H., Huang, Q., Rong, W., Song, Y., Li, J., Wang, J., Chen, J., & Li, L. (2019). Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms. Computational and Mathematical Methods in Medicine, 2019, 1–10. https://doi.org/10.1155/2019/2717454
Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., & Li, L. (2016). Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning. Scientific Reports, 6(1). https://doi.org/10.1038/srep27327
TCIA Citation
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7
Other Publications Using This Data
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Version 1 (Current): Updated yyyy/mm/dd
Data Type | Download all or Query/Filter |
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Images (DICOM, 22.9 GB) | (Requires NBIA Data Retriever.) |