{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:05:20Z","timestamp":1785405920477,"version":"3.56.0"},"reference-count":24,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Australian Commonwealth Department of Health and Aged Care"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Cancer Control"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Early breast cancer detection through periodic screening is crucial for reducing mortality due to later stage detection. Microcalcifications are common mammographic findings, present in both malignant lesions, benign pathologies, and normal tissues. This study aimed to assess radiologists\u2019 observer performance in determining suspicious calcifications requiring recall, and to compare reader performance to an in-house AI model trained and tested on Australian screening mammograms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this pilot proof-of-concept cross-sectional study, radiologists (n=27), breast physicians (n=2), and final year radiology trainees (n=6), completed the same mammographic test set consisting of 30 mammographic cases displaying different types of calcifications (10 breast cancer, 20 normal\/benign). An in-house trained artificial intelligence model (\n                      <jats:italic toggle=\"yes\">Sydney-GMIC)<\/jats:italic>\n                      was also applied to the same test set. Performance between readers was compared to AI via Spearman Rank-Order Correlation test. Work experience and caseload trends were compared using independent T tests and Mann-Whitney-U.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Sensitivity was significantly higher in radiologists with \u2264 10 years of experience compared to radiology trainees (72.2% vs 53.3%,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      =0.042). Furthermore, readers with higher cases read per week (CPW) (i.e. 101-200 CPW compared to 0-20 CPW) had a decreased specificity (58.8% vs 74.6%,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      =0.041), but higher sensitivity (68.8% vs 59.2%,\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      =0.12). In this pilot dataset, the\n                      <jats:italic toggle=\"yes\">Sydney-GMIC<\/jats:italic>\n                      AI model demonstrated higher sensitivity and specificity than the radiologist means. The cases perceived as difficult by AI differed substantially from those challenging for human readers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study highlights the challenging nature of recalling calcifications from screening mammograms only, with variable performance among readers. The findings of this small pilot study are exploratory in nature, but the AI performance signals the potential utility of AI models in mammographic analysis of screening cases to progress to recall.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10732748261470916","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T18:40:27Z","timestamp":1785177627000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparing Radiologists\u2019 and Artificial Intelligence Performance Detecting Suspicious Microcalcifications on Screening Mammograms: A Pilot Cross-Sectional Study"],"prefix":"10.1177","volume":"33","author":[{"given":"Sarah J.","family":"Lewis","sequence":"first","affiliation":[{"name":"The University of Sydney"},{"name":"Western Sydney University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayden B.","family":"Wells","sequence":"additional","affiliation":[{"name":"The University of Sydney"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengqiang","family":"Jiang","sequence":"additional","affiliation":[{"name":"The University of Sydney"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4327-0724","authenticated-orcid":false,"given":"Dania Abu","family":"Awwad","sequence":"additional","affiliation":[{"name":"The University of Sydney"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melissa L.","family":"Barron","sequence":"additional","affiliation":[{"name":"The University of Sydney"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phuong Dung (Yun)","family":"Trieu","sequence":"additional","affiliation":[{"name":"The University of Sydney"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"e_1_3_6_2_2","volume-title":"Cancer data in Australia, overview of cancer in Australia","year":"2023","unstructured":"Cancer data in Australia, overview of cancer in Australia, 2023. Australian Institute of Health and Welfare. Available at: https:\/\/www.aihw.gov.au\/reports\/cancer\/cancer-data-in-australia\/contents\/overview-of-cancer-in-australia-2023 (Accessed: 19 June 2024)."},{"key":"e_1_3_6_3_2","author":"National Breast Cancer Foundation (NBCF)","year":"2023","unstructured":"National Breast Cancer Foundation (NBCF). Breast cancer statistics. 2023. Available at: https:\/\/nbcf.org.au\/about-breast-cancer\/breast-cancer-stats\/#:\u223c:text=The_5%2Dyear_survival_rate,is_significantly_lower_at_32%25","journal-title":"Breast cancer statistics"},{"key":"e_1_3_6_4_2","unstructured":"Australian Government Department of Health and Aged Care. Breastscreen Australia Program Australian Government Department of Health and Aged Care. 2024. Available at: https:\/\/www.health.gov.au\/our-work\/breastscreen-australia-program"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.3892\/mco.2022.2514"},{"key":"e_1_3_6_6_2","volume-title":"StatPearls [Internet]","author":"Tomlinson-Hansen SE","year":"2026","unstructured":"Tomlinson-Hansen SE, Khan M, Cassaro S. Breast Ductal Carcinoma in Situ. In: StatPearls [Internet]. StatPearls Publishing; 2026. [Updated 2023 Feb 27]Available from: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK567766\/"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0169349"},{"key":"e_1_3_6_8_2","volume-title":"StatPearls [Internet]","author":"Reeves RA","year":"2024","unstructured":"Reeves RA, Kaufman T. Mammography. In: StatPearls [Internet]. StatPearls Publishing; 2024. [Updated 2023 Jul 24] Available from: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK559310\/"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20160594"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.240184"},{"key":"e_1_3_6_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101908"},{"key":"e_1_3_6_12_2","doi-asserted-by":"publisher","DOI":"10.1186\/s43055-026-01682-7"},{"key":"e_1_3_6_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-62324-4"},{"key":"e_1_3_6_14_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2024.21972"},{"key":"e_1_3_6_15_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.39335.541782.AD"},{"key":"e_1_3_6_16_2","volume-title":"The NYU Breast Cancer Screening Dataset v1.0","author":"Wu N","year":"2019","unstructured":"Wu N, Phang J, Park J, et al. In:The NYU Breast Cancer Screening Dataset v1.0: Technical Report; 2019."},{"key":"e_1_3_6_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16020322"},{"key":"e_1_3_6_18_2","doi-asserted-by":"crossref","unstructured":"He K Zhang X Ren S Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. USA: Las Vegas; 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_6_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfa.2013.05.001"},{"key":"e_1_3_6_20_2","unstructured":"Kingma DP Ba AJ. A method for stochastic optimization. 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