{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:34:38Z","timestamp":1760146478867,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:00:00Z","timestamp":1731024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>This study evaluates the potential of an AI-based computer-aided detection (AI-CAD) system in digital mammography for predicting pathologic complete response (pCR) in breast cancer patients after neoadjuvant chemotherapy (NAC). A retrospective analysis of 132 patients who underwent NAC and surgery between January 2020 and December 2022 was performed. Pre- and post-NAC mammograms were analyzed using conventional CAD and AI-CAD systems, with negative exams defined by the absence of marked abnormalities. Two radiologists reviewed mammography, ultrasound, MRI, and diffusion-weighted imaging (DWI). Concordance rates between CAD and AI-CAD were calculated, and the diagnostic performance, including the area under the receiver operating characteristics curve (AUC), was assessed. The pre-NAC concordance rates were 90.9% for CAD and 97% for AI-CAD, while post-NAC rates were 88.6% for CAD and 89.4% for AI-CAD. The MRI had the highest diagnostic performance for pCR prediction, with AI-CAD performing comparably to other modalities. Univariate analysis identified significant predictors of pCR, including AI-CAD, mammography, ultrasound, MRI, histologic grade, ER, PR, HER2, and Ki-67. In multivariable analysis, negative MRI, histologic grade 3, and HER2 positivity remained significant predictors. In conclusion, this study demonstrates that AI-CAD in digital mammography shows the potential to examine the pCR of breast cancer patients following NAC.<\/jats:p>","DOI":"10.3390\/life14111449","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T04:02:54Z","timestamp":1731038574000},"page":"1449","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy"],"prefix":"10.3390","volume":"14","author":[{"given":"Ga Eun","family":"Park","sequence":"first","affiliation":[{"name":"Department of Radiology, Seoul Saint Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5991-6035","authenticated-orcid":false,"given":"Bong Joo","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul Saint Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4478-9720","authenticated-orcid":false,"given":"Sung Hun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul Saint Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4718-1788","authenticated-orcid":false,"given":"Han Song","family":"Mun","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul Saint Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1148\/radiol.2018172868","article-title":"Dynamic contrast-enhanced breast MRI for evaluating residual tumor size after neoadjuvant chemotherapy","volume":"289","author":"Cho","year":"2018","journal-title":"Radiology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/nrclinonc.2015.63","article-title":"Surgical issues in patients with breast cancer receiving neoadjuvant chemotherapy","volume":"12","author":"King","year":"2015","journal-title":"Nat. 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