{"id":1513,"date":"2024-09-15T20:11:18","date_gmt":"2024-09-15T20:11:18","guid":{"rendered":"https:\/\/www.lunit.io\/publication\/artificial-intelligence-based-computer-aided-diagnosis-abnormality-score-trends-in-the-serial-mammography-of-patients-with-breast-cancer\/"},"modified":"2025-11-02T12:05:44","modified_gmt":"2025-11-02T12:05:44","slug":"artificial-intelligence-based-computer-aided-diagnosis-abnormality-score-trends-in-the-serial-mammography-of-patients-with-breast-cancer","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/en\/publication\/artificial-intelligence-based-computer-aided-diagnosis-abnormality-score-trends-in-the-serial-mammography-of-patients-with-breast-cancer\/","title":{"rendered":"Artificial intelligence-based computer-aided diagnosis abnormality score trends in the serial mammography of patients with breast cancer"},"content":{"rendered":"

Artificial intelligence-based computer-aided diagnosis abnormality score trends in the serial mammography of patients with breast cancer<\/h3>\n

Si Eun Lee, Kyunghwa Han, Miribi Rho, Eun-Kyung Kim<\/p>\n

European Journal of Radiology, 2024<\/strong><\/p>\n

Abstract<\/strong>
\nPurpose<\/strong>
\nTo explore the abnormality score trends of artificial intelligence-based computer-aided diagnosis (AI-CAD) in the serial mammography of patients until a final diagnosis of breast cancer.<\/p>\n

Method<\/strong>
\nFrom 2015 to 2019, 126 breast cancer patients who had at least two previous mammograms obtained from 2008 up to cancer diagnosis were included. AI-CAD was retrospectively applied to 487 previous mammograms and all the abnormality scores calculated by AI-CAD were obtained. The contralateral breast of each affected breast was defined as the control group. We divided all mammograms by 6-month intervals from cancer diagnosis in reverse chronological order. The random coefficient model was used to estimate whether the chronological trend of AI-CAD abnormality scores differed between cancer and normal breasts. Subgroup analyses were performed according to mammographic visibility, invasiveness and molecular subtype of the invasive cancer.<\/p>\n

Results<\/strong>
\nMean period from initial examination to cancer diagnosis was 6.0 years (range 1.7\u201310.7 years). The abnormality scores of breasts diagnosed with cancer showed a significantly increasing trend during the previous examination period (slope 0.6 per 6 months, p for the slope < 0.001), while the contralateral normal breast showed no trend (slope 0.03, p = 0.776). The difference in slope between the cancerous and contralateral breasts was significant (p < 0.001). For mammography-visible cancers, the abnormality scores in cancerous breasts showed a significant increasing trend (slope 0.8, p < 0.001), while for mammography-occult cancers, the trend was not significant (slope 0.1, p = 0.6). For invasive cancers, the slope of the abnormality scores showed a significant increasing trend (slope 1.4, p = 0.002), unlike ductal carcinoma in situ (DCIS) which showed no significant trend. There was no significant difference in the slope of abnormality scores among the subtypes of invasive cancers (p = 0.418).<\/p>\n

Conclusion<\/strong>
\nBreasts diagnosed with cancer showed an increase in AI-CAD abnormality scores in previous serial mammograms, suggesting that AI-CAD could be useful for early detection of breast cancer.<\/p>\n

Read the full paper<\/strong><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[],"publication-region":[87],"publication-type":[],"radiology":[97,99,96],"class_list":["post-1513","publication","type-publication","status-publish","hentry","publication-region-asia","radiology-breast","radiology-earlier-detection-including-interval-cancers","radiology-lunit-insight"],"acf":[],"yoast_head":"\nArtificial intelligence-based computer-aided diagnosis abnormality score trends in the serial mammography of patients with breast cancer - Lunit<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.lunit.io\/en\/publication\/artificial-intelligence-based-computer-aided-diagnosis-abnormality-score-trends-in-the-serial-mammography-of-patients-with-breast-cancer\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Artificial intelligence-based computer-aided diagnosis abnormality score trends in the serial mammography of patients with breast cancer - Lunit\" \/>\n<meta property=\"og:description\" content=\"Artificial intelligence-based computer-aided diagnosis abnormality score trends in the serial mammography of patients with breast cancer Si Eun Lee, Kyunghwa Han, Miribi Rho, Eun-Kyung Kim European Journal of Radiology, 2024 Abstract Purpose To explore the abnormality score trends of artificial intelligence-based computer-aided diagnosis (AI-CAD) in the serial mammography of patients until a final diagnosis of […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.lunit.io\/en\/publication\/artificial-intelligence-based-computer-aided-diagnosis-abnormality-score-trends-in-the-serial-mammography-of-patients-with-breast-cancer\/\" \/>\n<meta property=\"og:site_name\" content=\"Lunit\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-02T12:05:44+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@lunit_ai\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/artificial-intelligence-based-computer-aided-diagnosis-abnormality-score-trends-in-the-serial-mammography-of-patients-with-breast-cancer\\\/\",\"url\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/artificial-intelligence-based-computer-aided-diagnosis-abnormality-score-trends-in-the-serial-mammography-of-patients-with-breast-cancer\\\/\",\"name\":\"Artificial intelligence-based computer-aided diagnosis abnormality score trends in the serial mammography of patients with breast cancer - 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