Location of AI risk markers and associated mammographic features in screening mammograms obtained years before screen-detected breast cancer.
Authors
Affiliations (9)
Affiliations (9)
- Department of Radiology and Nuclear Medicine, Østfold Hospital Trust, Kalnes, Norway.
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- Department of Breast Cancer Screening, Cancer Registry, Norwegian Institute of Public Health, Oslo, Norway.
- Department of Radiology, Division of Diagnostics, Møre og Romsdal Hospital Trust, Ålesund, Norway.
- Department of Health Sciences in Ålesund, Faculty of Medicine and Health Scienses, Norwegian University of Science and Technology, Trondheim, Norway.
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway.
- Norwegian Computing Center, Oslo, Norway.
- Department of Breast Cancer Screening, Cancer Registry, Norwegian Institute of Public Health, Oslo, Norway. [email protected].
- Department of Health and Care Sciences, UiT, The Artic University of Norway, Tromsø, Norway. [email protected].
Abstract
To examine screening mammograms assigned high-risk scores by two artificial intelligence (AI) models, 2 and 4 years prior to screen-detected cancers. This retrospective study was based on data from 130,031 screening examinations performed in BreastScreen Norway (2008-2018), processed by two AI models: a commercial model (A) and an in-house model (B). The study sample included women with screen-detected cancer, and two prior consecutive screening examinations assigned the highest 10% of AI risk scores. Two radiologists conducted an informed review of three consecutive screening mammograms per woman, 4 and 2 years prior to, and at diagnosis. Descriptive statistics assessed the location of AI markings, mammographic features, histopathological tumor characteristics, and visibility of malignancy preceding diagnosis. Model A and B assigned high-risk scores at both prior mammograms for 43 and 47 cases, respectively, with 29 cases selected by both, yielding 61 sets of three consecutive examinations. AI markings corresponded to the cancer location in at least one view in 61% and 57% of cases 4 years prior to diagnosis for models A and B, respectively, while radiologists classified 89% as true negative or minimal sign non-specific. At diagnosis, spiculated mass (28%) and density with calcifications (20%) were the most frequent mammographic features but were absent 2 and 4 years prior. The AI models identified breast cancer in a substantial part of screening mammograms 2 and 4 years preceding diagnosis, but only a few demonstrated suspicious findings according to radiologists. Question Understanding the correspondence between AI markings, cancer location, and mammographic features is important to evaluate AI models' potential to enhance early breast cancer detection. Findings AI markings corresponded to the cancer location 4 and 2 years prior to diagnosis, with evolving mammographic features, yet radiologists classified most examinations as negative. Clinical relevance The discrepancy between AI-identified breast cancers years before diagnosis and radiologists classifying the same cases as negative highlights both AI's potential and the challenges of how best to implement it to enhance early detection.