{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:23:43Z","timestamp":1790267023516,"version":"4.1.0"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016079","name":"Science and Technology Department of Xinjiang Uygur Autonomous Region","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016079","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate an artificial intelligence (AI)-driven ultrasound radiomics model for predicting postoperative recurrence and metastasis in patients with triple-negative breast cancer (TNBC) receiving standardized therapies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective study of 668 female TNBC patients (treated 2013-2018). Univariate and multivariate logistic regression were first used to screen significant clinicopathological variables for baseline assessment and to inform model development. Radiomic features were automatically extracted from\u00a0pretreatment ultrasound images using PyRadiomics following tumor segmentation. A radiomics signature was constructed by integrating LASSO for feature selection with a support vector machine (SVM) classifier. The model\u2019s performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), and confusion matrices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The ultrasound radiomics model showed high predictive accuracy for any recurrence\/metastasis, with an AUC of 0.9458 in the training cohort and 0.8983 in the validation cohort. For distinguishing between locoregional recurrence and distant metastasis, the model achieved AUCs of 0.9341 and 0.8824 in the training and validation cohorts, respectively. Calibration and decision curve analyses confirmed the model\u2019s robust predictive capability and potential clinical utility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates that an AI-enhanced ultrasound radiomics model can effectively predict postoperative recurrence and metastasis patterns in TNBC, offering a promising non-invasive tool to support personalized prognosis assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1654953","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T05:19:28Z","timestamp":1771910368000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Prognostic prediction model for triple-negative breast cancer using artificial intelligence and ultrasound radiomics"],"prefix":"10.3389","volume":"16","author":[{"given":"Yushanjiang","family":"Zilalan","sequence":"first","affiliation":[{"name":"Department of Breast & Thyroid Surgery, Xinjiang Medical University Affiliated Tumor Hospital","place":["Urumqi, Xinjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Breast & Thyroid Surgery, Xinjiang Medical University Affiliated Tumor Hospital","place":["Urumqi, Xinjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Sha","sequence":"additional","affiliation":[{"name":"Department of Breast & Thyroid Surgery, Xinjiang Medical University Affiliated Tumor Hospital","place":["Urumqi, Xinjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Liu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Smart Mine Engineering Technology Center, Xinjiang Institute of Engineering","place":["Urumqi, Xinjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongtao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Breast & Thyroid Surgery, Xinjiang Medical University Affiliated Tumor Hospital","place":["Urumqi, Xinjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J Clin"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.annonc.2023.08.017","article-title":"Understanding breast cancer complexity to improve patient outcomes: The St Gallen International Consensus Conference for the Primary 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