CSAW-CC (mammography) – a dataset for AI research to improve screening, diagnostics and prognostics of breast cancer
The dataset contains x-ray images, mammography, from breast cancer screening at the Karolinska University Hospital, Stockholm, Sweden, collected by principal investigator Fredrik Strand at Karolinska Institutet. The purpose for compiling the dataset was to perform AI research to improve screening, diagnostics and prognostics of breast cancer. The dataset is based on a selection of cases with and without a breast cancer diagnosis, taken from a more comprehensive source dataset. 1,103 cases of first-time breast cancer for women in the screening age range (40-74 years) during the included time period (November 2008 to December 2015) were included. Of these, a random selection of 873 cases have been included in the published dataset. A random selection of 10,000 healthy controls during the same time period were included. Of these, a random selection of 7,850 cases have been included in the published dataset. For each individual all screening mammograms, also repeated over time, were included; as well as the date of screening and the age. In addition, there are pixel-level annotations of the tumors created by a breast radiologist (small lesions such as micro-calcifications have been annotated as an area). Annotations were also drawn in mammograms prior to diagnosis; if these contain a single pixel it means no cancer was seen but the estimated location of the center of the future cancer was shown by a single pixel annotation. In addition to images, the dataset also contains cancer data created at the Karolinska University Hospital and extracted through the Regional Cancer Center Stockholm-Gotland. This data contains information about the time of diagnosis and cancer characteristics including tumor size, histology and lymph node metastasis. The precision of non-image data was decreased, through categorisation and jittering, to ensure that no single individual can be identified. The following types of files are available: - CSV: The following data is included (if applicable): cancer/no cancer (meaning breast cancer during 2008 to 2015), age group at screening, days from image to diagnosis (if any), cancer histology, cancer size group, ipsilateral axillary lymph node metastasis. There is one csv file for the entire dataset, with one row per image. Any information about cancer diagnosis is repeated for all rows for an individual who was diagnosed (i.e., it is also included in rows before diagnosis). For each exam date there is the assessment by radiologist 1, radiologist 2 and the consensus decision. - DICOM: Mammograms. For each screening, four images for the standard views were acuqired: left and right, mediolateral oblique and craniocaudal. There should be four files per examination date. - PNG: Cancer annotations. For each DICOM image containing a visible tumor. Access: The dataset is available upon request due to the size of the material. The image files in DICOM and PNG format comprises approximately 2.5 TB. Access to the CSV file including parametric data is possible via download as associated documentation.
Cohort of Screen-age Women - Case control (CSAW-CC)
Dembrower, K., Liu, Y., Azizpour, H., Eklund, M., Smith, K., Lindholm, P., & Strand, F. (2020). Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction. Radiology, 294(2), 265–272. https://doi.org/10.1148/radiol.2019190872
Dembrower K, Lindholm P, Strand F. A Multi-million Mammography Image Dataset and Population-Based Screening Cohort for the Training and Evaluation of Deep Neural Networks-the Cohort of Screen-Aged Women (CSAW). J Digit Imaging. 2019.
Dembrower, K., Wahlin, E., Liu, Y., Salim, M., Smith, K., Lindholm, P., Eklund, M., & Strand, F. (2020). Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload : a retrospective simulation study. The Lancet Digital Health, 2(9), E468–E474. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281510
Salim, M., Wåhlin, E., Dembrower, K., Azavedo, E., Foukakis, T., Liu, Y., Smith, K., Eklund, M., & Strand, F. (2020). External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms. JAMA Oncology, 6(10), 1581. https://doi.org/10.1001/jamaoncol.2020.3321
SciLifeLab - Royal Institute of Technology
Regional Cancer Centre Stockholm-Gotland
Purpose and goal: To develop and test an AI-system that analyzes mammography images and which can be used to improve the breast cancer screening process. To achieve this, we have built a a database with mammography images in combination with clinical data from the breast cancer registry.