Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection
Introduction Artifi cial intelligence (AI) algorithms for interpreting mammograms have the potential to improve the effectiveness of population breast cancer screening programmes if they can detect cancers, including interval cancers, without contributing substantially to overdiagnosis. Studies sugge...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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BMJ PUBLISHING GROUP
2022
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| Online Access: | http://purl.org/au-research/grants/nhmrc/1099655 http://hdl.handle.net/20.500.11937/91327 |
| _version_ | 1848765508932861952 |
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| author | Marinovich, Luke Wylie, E. Lotter, W. Pearce, A. Carter, S.M. Lund, H. Waddell, A. Kim, J.G. Pereira, Gavin Lee, C.I. Zackrisson, S. Brennan, M. Houssami, N. |
| author_facet | Marinovich, Luke Wylie, E. Lotter, W. Pearce, A. Carter, S.M. Lund, H. Waddell, A. Kim, J.G. Pereira, Gavin Lee, C.I. Zackrisson, S. Brennan, M. Houssami, N. |
| author_sort | Marinovich, Luke |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Introduction Artifi cial intelligence (AI) algorithms for interpreting mammograms have the potential to improve the effectiveness of population breast cancer screening programmes if they can detect cancers, including interval cancers, without contributing substantially to overdiagnosis. Studies suggesting that AI has comparable or greater accuracy than radiologists commonly employ € enriched' datasets in which cancer prevalence is higher than in population screening. Routine screening outcome metrics (cancer detection and recall rates) cannot be estimated from these datasets, and accuracy estimates may be subject to spectrum bias which limits generalisabilty to real-world screening. We aim to address these limitations by comparing the accuracy of AI and radiologists in a cohort of consecutive of women attending a real-world population breast cancer screening programme. Methods and analysis A retrospective, consecutive cohort of digital mammography screens from 109 000 distinct women was assembled from BreastScreen WA (BSWA), Western Australia's biennial population screening programme, from November 2016 to December 2017. The cohort includes 761 screen-detected and 235 interval cancers. Descriptive characteristics and results of radiologist double-reading will be extracted from BSWA outcomes data collection. Mammograms will be reinterpreted by a commercial AI algorithm (DeepHealth). AI accuracy will be compared with that of radiologist single-reading based on the di €erence in the area under the receiver operating characteristic curve. Cancer detection and recall rates for combined AI-radiologist reading will be estimated by pairing the first radiologist read per screen with the AI algorithm, and compared with estimates for radiologist double-reading. Ethics and dissemination This study has ethical approval from the Women and Newborn Health Service Ethics Committee (EC00350) and the Curtin University Human Research Ethics Committee (HRE2020-0316). Findings will be published in peer-reviewed journals and presented at national and international conferences. Results will also be disseminated to stakeholders in Australian breast cancer screening programmes and policy makers in population screening. |
| first_indexed | 2025-11-14T11:36:22Z |
| format | Journal Article |
| id | curtin-20.500.11937-91327 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:36:22Z |
| publishDate | 2022 |
| publisher | BMJ PUBLISHING GROUP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-913272023-04-19T07:24:54Z Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection Marinovich, Luke Wylie, E. Lotter, W. Pearce, A. Carter, S.M. Lund, H. Waddell, A. Kim, J.G. Pereira, Gavin Lee, C.I. Zackrisson, S. Brennan, M. Houssami, N. Science & Technology Life Sciences & Biomedicine Medicine, General & Internal General & Internal Medicine breast tumours breast imaging diagnostic radiology breast imaging breast tumours diagnostic radiology Artificial Intelligence Australia Breast Neoplasms Cohort Studies Early Detection of Cancer Female Humans Infant, Newborn Mammography Mass Screening Retrospective Studies Humans Breast Neoplasms Mammography Mass Screening Retrospective Studies Cohort Studies Artificial Intelligence Infant, Newborn Australia Female Early Detection of Cancer Introduction Artifi cial intelligence (AI) algorithms for interpreting mammograms have the potential to improve the effectiveness of population breast cancer screening programmes if they can detect cancers, including interval cancers, without contributing substantially to overdiagnosis. Studies suggesting that AI has comparable or greater accuracy than radiologists commonly employ € enriched' datasets in which cancer prevalence is higher than in population screening. Routine screening outcome metrics (cancer detection and recall rates) cannot be estimated from these datasets, and accuracy estimates may be subject to spectrum bias which limits generalisabilty to real-world screening. We aim to address these limitations by comparing the accuracy of AI and radiologists in a cohort of consecutive of women attending a real-world population breast cancer screening programme. Methods and analysis A retrospective, consecutive cohort of digital mammography screens from 109 000 distinct women was assembled from BreastScreen WA (BSWA), Western Australia's biennial population screening programme, from November 2016 to December 2017. The cohort includes 761 screen-detected and 235 interval cancers. Descriptive characteristics and results of radiologist double-reading will be extracted from BSWA outcomes data collection. Mammograms will be reinterpreted by a commercial AI algorithm (DeepHealth). AI accuracy will be compared with that of radiologist single-reading based on the di €erence in the area under the receiver operating characteristic curve. Cancer detection and recall rates for combined AI-radiologist reading will be estimated by pairing the first radiologist read per screen with the AI algorithm, and compared with estimates for radiologist double-reading. Ethics and dissemination This study has ethical approval from the Women and Newborn Health Service Ethics Committee (EC00350) and the Curtin University Human Research Ethics Committee (HRE2020-0316). Findings will be published in peer-reviewed journals and presented at national and international conferences. Results will also be disseminated to stakeholders in Australian breast cancer screening programmes and policy makers in population screening. 2022 Journal Article http://hdl.handle.net/20.500.11937/91327 10.1136/bmjopen-2021-054005 English http://purl.org/au-research/grants/nhmrc/1099655 http://purl.org/au-research/grants/nhmrc/1173991 http://purl.org/au-research/grants/nhmrc/1181960 http://purl.org/au-research/grants/nhmrc/1194410 http://creativecommons.org/licenses/by-nc/4.0/ BMJ PUBLISHING GROUP fulltext |
| spellingShingle | Science & Technology Life Sciences & Biomedicine Medicine, General & Internal General & Internal Medicine breast tumours breast imaging diagnostic radiology breast imaging breast tumours diagnostic radiology Artificial Intelligence Australia Breast Neoplasms Cohort Studies Early Detection of Cancer Female Humans Infant, Newborn Mammography Mass Screening Retrospective Studies Humans Breast Neoplasms Mammography Mass Screening Retrospective Studies Cohort Studies Artificial Intelligence Infant, Newborn Australia Female Early Detection of Cancer Marinovich, Luke Wylie, E. Lotter, W. Pearce, A. Carter, S.M. Lund, H. Waddell, A. Kim, J.G. Pereira, Gavin Lee, C.I. Zackrisson, S. Brennan, M. Houssami, N. Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection |
| title | Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection |
| title_full | Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection |
| title_fullStr | Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection |
| title_full_unstemmed | Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection |
| title_short | Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection |
| title_sort | artificial intelligence (ai) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection |
| topic | Science & Technology Life Sciences & Biomedicine Medicine, General & Internal General & Internal Medicine breast tumours breast imaging diagnostic radiology breast imaging breast tumours diagnostic radiology Artificial Intelligence Australia Breast Neoplasms Cohort Studies Early Detection of Cancer Female Humans Infant, Newborn Mammography Mass Screening Retrospective Studies Humans Breast Neoplasms Mammography Mass Screening Retrospective Studies Cohort Studies Artificial Intelligence Infant, Newborn Australia Female Early Detection of Cancer |
| url | http://purl.org/au-research/grants/nhmrc/1099655 http://purl.org/au-research/grants/nhmrc/1099655 http://purl.org/au-research/grants/nhmrc/1099655 http://purl.org/au-research/grants/nhmrc/1099655 http://hdl.handle.net/20.500.11937/91327 |