{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T17:16:14Z","timestamp":1788023774839,"version":"build-2784847793"},"reference-count":39,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Conventional breast cancer screening is limited by cost, access, and patient discomfort. Whether a lightweight machine-learning model trained on routine laboratory indicators can support opportunistic screening remains unsettled.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective cohort of 13,285 individuals (1,216 ICD-10 C50 cases, 12,069 controls) from a Chinese tertiary hospital during 2022 to 2024 was analysed with a leakage-controlled pipeline. Six classical learners and LightGBM were trained on twenty indicators, then assessed by discrimination, calibration, decision-curve analysis, and zero-shot UK Biobank validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The lightweight LightGBM achieved an internal AUC of 0.987 with sensitivity 0.835, specificity 0.983, Brier 0.026, and an external AUC of 0.83, training in 5.6 s within 1.65 MB.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The model is a candidate low-cost triage tool, pending multicentre prospective validation and recalibration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/14604582261477932","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T17:40:12Z","timestamp":1787766012000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A lightweight machine learning approach for predicting breast cancer risk based on routine clinical indicators in the Chinese population"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6202-0510","authenticated-orcid":false,"given":"Duo","family":"Dai","sequence":"first","affiliation":[{"name":"Hospital Information Center, Shaanxi Provincial People\u2019s Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Ma","sequence":"additional","affiliation":[{"name":"Hospital Information Center, Shaanxi Provincial People\u2019s Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.breast.2022.08.010"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jncc.2024.01.006"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clbc.2023.06.004"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102276"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.semnuclmed.2022.02.003"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocw042"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0226765"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0146413"},{"key":"e_1_3_6_11_2","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg SM","year":"2017","unstructured":"Lundberg SM, Lee SI. 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