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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats: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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats: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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Understanding the correspondence between AI markings, cancer location, and mammographic features is important to evaluate AI models\u2019 potential to enhance early breast cancer detection.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>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.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>The discrepancy between AI-identified breast cancers years before diagnosis and radiologists classifying the same cases as negative highlights both AI\u2019s potential and the challenges of how best to implement it to enhance early detection.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12689-z","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T13:52:04Z","timestamp":1781790724000},"page":"8380-8390","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Location of AI risk markers and associated mammographic features in screening mammograms obtained years before screen-detected breast cancer"],"prefix":"10.1007","volume":"36","author":[{"given":"Marit A.","family":"Martiniussen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie B.","family":"Bergan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Merete U.","family":"Kristiansen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Solveig Roth","family":"Hoff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henrik Wethe","family":"Koch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fredrik A.","family":"Dahl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0178-8939","authenticated-orcid":false,"given":"Solveig","family":"Hofvind","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"12689_CR1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3322\/caac.21834","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray F, Laversanne M, Sung H et al (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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None of the authors were employees of or consultants for Lunit, which had no access to the data and were not involved in any part of the study. One author is an employee of the Norwegian Computing Center (NCC), but did not have an impact on the study sample. The remaining authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"One of the authors has significant statistical expertise (M.B.B.).","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The data were disclosed with legal basis in the Personal Health Data Filing system Act Section 19a-h [\n                      \n                      ] and the Cancer Registry Regulation Section 3\u20131 [\n                      \n                      ].","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"This study was approved by the Regional Committees for Medical and Health Research Ethics (#13294, #11022).","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The study population of 130,031 screening examinations performed in \u00d8stfold as part of BreastScreen Norway, 2008\u20132018, overlaps with two other studies. The first study was published in\n                      Radiology: Artificial Intelligence\n                      (Martiniussen et al [\n                      \n                      ]) and explored the overall performance of the same two AI models as used in the current study (Lunit INSIGHT MMG version 1.1.7.2 and an in-house model from the Norwegian Computing Center and the Cancer Registry of Norway). The second study was submitted to\n                      European Radiology\n                      on December 11, 2025. No analysis overlapped.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}