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Results show AI-marked prior findings in 54% (n=555) and targeted 904 (88%) carcinomas in follow-up. Sensitivity reached 73.4%, specificity 89%, and accuracy 78.4%. AI performance was highest for distortion and grouped microcalcifications, supporting enhanced early-change detection and follow-up guidance.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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problem does the study address?","Question",{"text":63,"@type":64},"It addresses breast malignancies that were previously missed on digital mammograms and later recognized retrospectively as cancer despite being considered negative or benign.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How was the AI used to flag potential missed cancers?",{"text":68,"@type":64},"The AI overlaid color hue on mammograms and produced a percentage suspicion score, highlighting abnormalities associated with missed early morphology indicators.",{"name":70,"@type":61,"acceptedAnswer":71},"What diagnostic performance did AI achieve in previously negative or benign cases?",{"text":72,"@type":64},"AI showed sensitivity of 73.4%, specificity of 89%, and accuracy of 78.4% for predicting malignancy in mammograms previously assigned as negative or 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100629  \nContents lists available at ScienceDirect  \nEuropean Journal of Radiology Open  \njournal [homepage:](homepage: www.elsevier.com/locate/ejro)[ www.elsevier.com/locate/ejro](homepage: www.elsevier.com/locate/ejro)  \n| Enhancing detection of previously missed non-palpable breast carcinomas through artificial intelligence\u003Cbr>Sahar Mansour a,d,*, Rasha Kamala,d, Samar Ahmed Hussein b, Mostafa Emarab,\u003Cbr>Yomna Kassabb,d, Sherif Nasser Tahac,d, Mohammed Mohammed Mohammed Gomaab,d\u003Cbr>a Women’s Imaging Unit, Radiology Department – Kasr ElAiny Hospital– Cairo University, Baheya center for early breast cancer detection and treatment, Egypt\u003Cbr>b Radiology Department, National Cancer Institute, Cairo University, Egypt c Surgery Department, National cancer institute – Cairo University, Egypt d Baheya center for early breast cancer detection and treatment, Egypt |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Digital mammography Artificial intelligence Breast cancer\u003Cbr>Missed carcinoma Asymmetry, subtle findings |  | Purpose: To investigate the impact of artificial intelligence (AI) reading digital mammograms in increasing the chance of detecting missed breast cancer, by studying the AI-flagged early morphology indictors, overlooked by the radiologist, and correlating them with the missed cancer pathology types.\u003Cbr>Methods and materials: Mammograms done in 2020–2023, presenting breast carcinomas (n = 1998), were analyzed in concordance with the prior one year’s result (2019–2022) assumed negative or benign. Present mammograms reviewed for the descriptors: asymmetry, distortion, mass, and microcalcifications. The AI presented abnormalities by overlaying color hue and scoring percentage for the degree of suspicion of malignancy. Results: Prior mammogram with AI marking compromised 54 %(n = 555), and in the present mammograms, AI targeted 904 (88 %) carcinomas. The descriptor proportion of “asymmetry” was the common presentation of missed breast carcinoma (64.1 %) in the prior mammograms and the highest detection rate for AI was presented by “distortion”(100 %) followed by “grouped microcalcifications”(80 %). AI performance to predict malignancy in previously assigned negative or benign mammograms showed sensitivity of 73.4 %, specificity of 89 %, and accuracy of 78.4 %.\u003Cbr>Conclusions: Reading mammograms with AI significantly enhances the detection of early cancerous changes, particularly in dense breast tissues. The AI’s detection rate does not correlate with specific pathological types of breast cancer, highlighting its broad utility. Subtle mammographic changes in postmenopausal women, not corroborated by ultrasound but marked by AI, warrant further evaluation by advanced applications of digital mammograms and close interval AI-reading mammogram follow up to minimize the potential for missed breast carcinoma. |\n\n1. Introduction  \nBreast malignancies are termed missed if they are retrospectively recognized in a previous mammogram and were considered negative, benign, or probably benign (in a prospective review) regardless of symptoms [1].  \nThere are several factors that contribute to the low sensitivity of digital mammograms; the most frequent one is the high density of the breast. Others include rapid tumor growth rate in correlation with annual follow-up and overlooked subtle perceptual or interpretive  \nerrors. About one-third of the newly detected early breast cancers were retrospectively localized in prior mammograms [2].  \nInterval cancers are known to have a worse prognosis than screendetected cancers, so keeping the rate as low as possible is critical for reducing breast cancer mortality [3,4].  \nArtificial Intelligence (AI) has shown significant promise in breast imaging, particularly in early cancer detection, risk assessment, and workflow optimization [5]. Combining a diagnostic AI system and mammographic texture model resulted in impr","cbCaint8tNI0ja68","https://ap.wps.com/l/cbCaint8tNI0ja68","pdf",9619696,"English","# Introduction\n# Methods\n## Study design\n## Image assessment and AI output\n## Performance metrics\n# Results\n# Conclusions","[{\"question\":\"What problem does the study address?\",\"answer\":\"It addresses breast malignancies that were previously missed on digital mammograms and later recognized retrospectively as cancer despite being considered negative or benign.\"},{\"question\":\"How was the AI used to flag potential missed cancers?\",\"answer\":\"The AI overlaid color hue on mammograms and produced a percentage suspicion score, highlighting abnormalities associated with missed early morphology indicators.\"},{\"question\":\"What diagnostic performance did AI achieve in previously negative or benign cases?\",\"answer\":\"AI showed sensitivity of 73.4%, specificity of 89%, and accuracy of 78.4% for predicting malignancy in mammograms previously assigned as negative or benign.\"}]","Enhancing detection of previously missed non-palpable breast carcinomas through artificial intelligence | PDF",1790000114,23]