{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T03:51:52Z","timestamp":1792813912324,"version":"4.5.0"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"1166","license":[{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>To investigate the impact of artificial intelligence (AI) on enhancing the sensitivity of digital mammograms in the detection and specification of grouped microcalcifications.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>The study is a retrospective analysis of grouped microcalcifications for 447 patients. Grouped microcalcifications detected were correlated with AI, which was applied to the initial mammograms. AI provided a heat map, demarcation, and quantitative evaluation for abnormalities according to the degree of suspicion of malignancy. Histopathology was the standard for confirmation of malignancy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>AI showed a high correlation percentage of 67.5% between the red colour of the colour hue bar and malignant microcalcifications (P-value &amp;lt;.001). The scoring of probable cancer was suggested (ie, more than 50% abnormality scoring) in 39.5% of true cancer lesions. The diagnostic performance of mammography for grouped microcalcifications revealed a sensitivity of 94.7% and a negative predictive value of 82.1%. False negatives were only 12 out of 228 that proved malignant calcifications. The agreement of cancer probability between standard mammograms and examinations read by AI presented a Kappa value of \u22120.094 and a P-value of &amp;lt;.001.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>The used AI system enhanced the sensitivity of mammograms in detecting suspicious microcalcifications, yet an expert human reader is required for proper specification.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Advances in knowledge<\/jats:title>\n                  <jats:p>Grouped calcifications could be early breast cancer on a mammogram. The morphology and distribution are correlated with the nature of breast diseases. AI is a potential decision support for the detection and classification of grouped microcalcifications and thus positively affects the control of breast cancer.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bjr\/tqae220","type":"journal-article","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T21:54:42Z","timestamp":1730238882000},"page":"246-253","source":"Crossref","is-referenced-by-count":6,"title":["Artificial intelligence reading digital mammogram: enhancing detection and differentiation of suspicious microcalcifications"],"prefix":"10.1093","volume":"98","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4581-9939","authenticated-orcid":false,"given":"Sahar","family":"Mansour","sequence":"first","affiliation":[{"name":"Women\u2019s Imaging Unit, Radiology Department, Kasr ElAiny Hospital, Cairo University , Cairo,","place":["Egypt"]},{"name":"Baheya Hospital for Early Breast Cancer and Treatment , Cairo,","place":["Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omnia","family":"Mokhtar","sequence":"additional","affiliation":[{"name":"Baheya Hospital for Early Breast Cancer and Treatment , Cairo,","place":["Egypt"]},{"name":"Radiology Department, National Cancer Institute, Cairo University , Cairo,","place":["Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mennat-Allah Samir Mohammed","family":"Abd El Galil","sequence":"additional","affiliation":[{"name":"Baheya Hospital for Early Breast Cancer and Treatment , Cairo,","place":["Egypt"]},{"name":"Radiology Department, National Cancer Institute, Cairo University , Cairo,","place":["Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sherif Nasser","family":"Taha","sequence":"additional","affiliation":[{"name":"Baheya Hospital for Early Breast Cancer and Treatment , Cairo,","place":["Egypt"]},{"name":"Surgery Department, National Cancer Institute, Cairo University , Cairo,","place":["Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ola Magdy Mohamed","family":"Shetat","sequence":"additional","affiliation":[{"name":"Baheya Hospital for Early Breast Cancer and Treatment , Cairo,","place":["Egypt"]},{"name":"Radiology Department, National Cancer Institute, Cairo University , Cairo,","place":["Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,10,29]]},"reference":[{"issue":"1","key":"2025012207510194600_tqae220-B1","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1186\/s12885-021-09133-4","article-title":"Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images","volume":"22","author":"Brahimetaj","year":"2022","journal-title":"BMC Cancer"},{"issue":"5","key":"2025012207510194600_tqae220-B2","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1038\/s41416-021-01459-x","article-title":"Mammographic microcalcifications and risk of breast cancer","volume":"125","author":"Azam","year":"2021","journal-title":"Br J Cancer"},{"key":"2025012207510194600_tqae220-B3","first-page":"172406","article-title":"Grouped amorphous calcifications at mammography: frequently atypical but rarely associated with aggressive malignancy radiology","author":"Oligane","year":"2018"},{"issue":"1128","key":"2025012207510194600_tqae220-B4","doi-asserted-by":"crossref","first-page":"20210820","DOI":"10.1259\/bjr.20210820","article-title":"Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?","volume":"94","author":"Mansour","year":"2021","journal-title":"Br J 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Impact of artificial intelligence decision support on breast ultrasound lesion assessment","volume":"214","author":"Mango","year":"2020","journal-title":"AJR Am J Roentgenol"},{"issue":"3","key":"2025012207510194600_tqae220-B13","doi-asserted-by":"crossref","first-page":"e138","DOI":"10.1016\/S2589-7500(20)30003-0","article-title":"Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study","volume":"2","author":"Kim","year":"2020","journal-title":"Lancet Digit Health"},{"issue":"2","key":"2025012207510194600_tqae220-B14","first-page":"000632","article-title":"Review article: breast calcifications on mammography","volume":"6","author":"Kameswari","year":"2021","journal-title":"NACS"},{"issue":"4","key":"2025012207510194600_tqae220-B15","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1097\/RLI.0000000000000729","article-title":"Classification of mammographic breast microcalcifications using a deep convolutional neural network: a BI-RADS-based approach","volume":"56","author":"Sch\u00f6nenberger","year":"2021","journal-title":"Invest Radiol"},{"issue":"12","key":"2025012207510194600_tqae220-B16","first-page":"2213","article-title":"Role of artificial intelligence in the assessment of mammographically-detected breast micro calcification","volume":"90","author":"Abdelrahman","year":"2020","journal-title":"Med J Cairo Univ"}],"container-title":["British Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bjr\/advance-article-pdf\/doi\/10.1093\/bjr\/tqae220\/60580222\/tqae220.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bjr\/article-pdf\/98\/1166\/246\/60580222\/tqae220.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bjr\/article-pdf\/98\/1166\/246\/60580222\/tqae220.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T07:51:13Z","timestamp":1737532273000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bjr\/article\/98\/1166\/246\/7849648"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,29]]},"references-count":16,"journal-issue":{"issue":"1166","published-online":{"date-parts":[[2024,10,29]]},"published-print":{"date-parts":[[2025,2,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bjr\/tqae220","relation":{},"ISSN":["0007-1285","1748-880X"],"issn-type":[{"value":"0007-1285","type":"print"},{"value":"1748-880X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,2]]},"published":{"date-parts":[[2024,10,29]]}}}