Alternative title
Cohort of Screen-age Women - Case control (CSAW-CC)
Creator/Principal investigator(s)
Fredrik Strand
- Karolinska Institutet, Department of Oncology-Pathology
Description
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 tu
Language
English
Research principal
Principal's reference number
4-3790/2016
Responsible department/unit
Department of Oncology-Pathology
Contributor(s)
Kevin Smith
- Royal Institute of Technology, SciLifeLab
Regional Cancer Centre Stockholm-Gotland
SciLifeLab - Royal Institute of Technology
Karolinska University Hospital, Breast Radiology
Data contains personal data
No
Ethics Review
Stockholm - Ref. 2016/2600-31
Swedish Ethical Review Authority - Ref. 2021-01030
Swedish Ethical Review Authority - Ref. 2019-03638
Swedish Ethical Review Authority - Ref. 2019-01946
Unit of analysis
Population
Women 40-74 years of age who were invited to mammography screening
Study design
Case-control study
Description of study design
Case-control cohort regarding breast cancer diagnosis. All 1103 cases of first-time breast cancer for women in the screening age range (40-74 years) during the included time period (late 2008 to Dec 31, 2015) were included. A random selection of 10,000 healthy controls during the same time period were included. Of these, a random selection of 873 of diagnosed cases and of 7850 healthy controls, designated “non-hidden”, have been included in the published dataset.
Sampling procedure
Controls: Randomly selected women who were not diagnosed with breast cancer before Dec 31, 2015.
Time period(s) investigated
2008 – 2015
Geographic spread
Geographic location: Stockholm County
Geographic description: The geographical uptake area of breast cancer screening at the Karolinska University Hospital in Stockholm, Sweden
Lowest geographic unit
Region
Highest geographic unit
Region
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
DOI:
https://doi.org/10.1148/radiol.2019190872
URN:
urn:nbn:se:kth:diva-267834
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.
DOI:
https://doi.org/10.1007/s10278-019-00278-0
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
DOI:
https://doi.org/10.1016/S2589-7500(20)30185-0
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
DOI:
https://doi.org/10.1001/jamaoncol.2020.3321
URN:
urn:nbn:se:kth:diva-284972
If you have published anything based on these data, please notify us with a reference to your publication(s). If you are responsible for the catalogue entry, you can update the metadata/data description in DORIS.
Associated documentation
Description
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 include
Version 1
https://doi.org/10.5878/45vm-t798
Citation
Download citation
Data format / data structure
Numeric
Text
Still image
Creator/Principal investigator(s)
Fredrik Strand
- Karolinska Institutet, Department of Oncology-Pathology
Keywords
Time period(s) investigated
2008 – 2015
Variables
19
Number of individuals/objects
8723
License
