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Objectives <p>To examine screening mammograms assigned high-risk scores by two artificial intelligence (AI) models, 2 and 4 years prior to screen-detected cancers.</p> Materials and methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Understanding the correspondence between AI markings, cancer location, and mammographic features is important to evaluate AI models’ potential to enhance early breast cancer detection.</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>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.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>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.</i></p> Graphical Abstract <p></p>

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Location of AI risk markers and associated mammographic features in screening mammograms obtained years before screen-detected breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月18日

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  • Marit A. Martiniussen,
  • Marie B. Bergan,
  • Merete U. Kristiansen,
  • Solveig Roth Hoff,
  • Henrik Wethe Koch,
  • Fredrik A. Dahl,
  • Solveig Hofvind

摘要

Objectives

To examine screening mammograms assigned high-risk scores by two artificial intelligence (AI) models, 2 and 4 years prior to screen-detected cancers.

Materials and methods

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.

Results

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.

Conclusion

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.

Key Points

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.

Graphical Abstract