Personalised breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence
(2020) 15th International Workshop on Breast Imaging, IWBI 2020 In Proceedings of SPIE - The International Society for Optical Engineering 11513.- Abstract
Breast cancer screening is predominantly performed using digital mammography (DM), but higher sensitivity has been demonstrated with digital breast tomosynthesis (DBT). A partial DBT screening in selected groups with a clear benefit from DBT might be more feasible than a full implementation, and using artificial intelligence (AI) to select women for DBT might be a possibility. This study used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately read DM and DBT. We retrospectively analysed DM examinations (n=14768) with a breast cancer detection software and used the provided risk score (1-10) for risk stratification. We tested how different score thresholds for adding DBT to... (More)
Breast cancer screening is predominantly performed using digital mammography (DM), but higher sensitivity has been demonstrated with digital breast tomosynthesis (DBT). A partial DBT screening in selected groups with a clear benefit from DBT might be more feasible than a full implementation, and using artificial intelligence (AI) to select women for DBT might be a possibility. This study used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately read DM and DBT. We retrospectively analysed DM examinations (n=14768) with a breast cancer detection software and used the provided risk score (1-10) for risk stratification. We tested how different score thresholds for adding DBT to an initial DM affects the number of detected cancers, additional DBT examinations needed, detection rate, and false positives. If using a threshold of 9.0, 25 (26 %) more cancers would be detected compared to using DM alone. Of the 41 cancers only detected on DBT, 61 % would be detected, with only 1797 (12 %) of the women examined with both DM and DBT. The detection rate for the added DBT would be 14/1000 women, while the false positive recalls would be increased with 58 (21 %). Using DBT only for selected high gain cases could be an alternative to a complete DBT screening. AI could be used for analysing DM to identify high gain cases, where DBT can be added during the same visit. There might be logistical challenges and further studies in a prospective setting are necessary.
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- author
- Dahlblom, Victor LU ; Tingberg, Anders LU ; Zackrisson, Sophia LU and Dustler, Magnus LU
- organization
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Artificial intelligence, Breast cancer screening, Digital breast tomosynthesis, Personalised screening
- host publication
- 15th International Workshop on Breast Imaging, IWBI 2020
- series title
- Proceedings of SPIE - The International Society for Optical Engineering
- editor
- Bosmans, Hilde ; Marshall, Nicholas and Van Ongeval, Chantal
- volume
- 11513
- article number
- 115130C
- publisher
- SPIE
- conference name
- 15th International Workshop on Breast Imaging, IWBI 2020
- conference location
- Leuven, Belgium
- conference dates
- 2020-05-25 - 2020-05-27
- external identifiers
-
- scopus:85086140189
- ISSN
- 0277-786X
- 1996-756X
- ISBN
- 9781510638310
- DOI
- 10.1117/12.2564344
- project
- Can breast cancer screening be improved with artificial intelligence?
- language
- English
- LU publication?
- yes
- id
- 30bfbe65-29f1-454a-a87c-44bab0cf3ceb
- date added to LUP
- 2021-01-11 12:13:39
- date last changed
- 2024-04-03 22:43:30
@inproceedings{30bfbe65-29f1-454a-a87c-44bab0cf3ceb, abstract = {{<p>Breast cancer screening is predominantly performed using digital mammography (DM), but higher sensitivity has been demonstrated with digital breast tomosynthesis (DBT). A partial DBT screening in selected groups with a clear benefit from DBT might be more feasible than a full implementation, and using artificial intelligence (AI) to select women for DBT might be a possibility. This study used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately read DM and DBT. We retrospectively analysed DM examinations (n=14768) with a breast cancer detection software and used the provided risk score (1-10) for risk stratification. We tested how different score thresholds for adding DBT to an initial DM affects the number of detected cancers, additional DBT examinations needed, detection rate, and false positives. If using a threshold of 9.0, 25 (26 %) more cancers would be detected compared to using DM alone. Of the 41 cancers only detected on DBT, 61 % would be detected, with only 1797 (12 %) of the women examined with both DM and DBT. The detection rate for the added DBT would be 14/1000 women, while the false positive recalls would be increased with 58 (21 %). Using DBT only for selected high gain cases could be an alternative to a complete DBT screening. AI could be used for analysing DM to identify high gain cases, where DBT can be added during the same visit. There might be logistical challenges and further studies in a prospective setting are necessary.</p>}}, author = {{Dahlblom, Victor and Tingberg, Anders and Zackrisson, Sophia and Dustler, Magnus}}, booktitle = {{15th International Workshop on Breast Imaging, IWBI 2020}}, editor = {{Bosmans, Hilde and Marshall, Nicholas and Van Ongeval, Chantal}}, isbn = {{9781510638310}}, issn = {{0277-786X}}, keywords = {{Artificial intelligence; Breast cancer screening; Digital breast tomosynthesis; Personalised screening}}, language = {{eng}}, publisher = {{SPIE}}, series = {{Proceedings of SPIE - The International Society for Optical Engineering}}, title = {{Personalised breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence}}, url = {{http://dx.doi.org/10.1117/12.2564344}}, doi = {{10.1117/12.2564344}}, volume = {{11513}}, year = {{2020}}, }