{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T07:19:26Z","timestamp":1787815166903,"version":"build-2784847793"},"reference-count":34,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004694","name":"Kaohsiung Medical University","doi-asserted-by":"publisher","award":["KMU\u2010TC114A206"],"award-info":[{"award-number":["KMU\u2010TC114A206"]}],"id":[{"id":"10.13039\/501100004694","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004694","name":"Kaohsiung Medical University","doi-asserted-by":"publisher","award":["KMU\u2010TC115A05\u20101"],"award-info":[{"award-number":["KMU\u2010TC115A05\u20101"]}],"id":[{"id":"10.13039\/501100004694","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Kaohsiung J of Med Scie"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>This study aimed to develop machine learning models for predicting tumor recurrence in breast cancer before neoadjuvant systemic therapy (NST) by integrating clinical and radiomic features derived from pretreatment computed tomography (CT). We retrospectively enrolled 235 patients with 237 breast tumors who underwent contrast\u2010enhanced CT before NST. Datasets were randomly divided into five\u2010fold training and testing sets using semi\u2010random partitioning to ensure similar clinical characteristics between the two subsets. Subsequently, a nested five\u2010fold cross\u2010validation was performed to develop a recurrence prediction model using three machine learning algorithms across clinical, radiomics, and integrated models. The performance of prediction models was compared using the area under the receiver\u2010operating characteristic curve (AUC), and the best clinical and radiomics models were further integrated to develop the final model. Kaplan\u2013Meier analysis with a log\u2010rank test was conducted to compare survival curves between high\u2010 and low\u2010risk groups stratified by the prediction models. The comparisons demonstrated that the random survival forest (RSF) clinical model (mean AUC\u2009=\u20090.755) and the Cox\u2010least absolute shrinkage and selection operator (Cox\u2010LASSO) radiomics model (mean AUC\u2009=\u20090.636) outperformed other machine learning algorithms. The integration of the clinical (RSF) and radiomics (Cox\u2010LASSO) models achieved a mean AUC of 0.777 in predicting tumor recurrence. The log\u2010rank analysis revealed significant differences in the survival curves between the high\u2010 and low\u2010risk groups stratified by the integration model on the testing sets. In conclusion, the integration of clinical and CT\u2010based radiomics models was helpful for the pretreatment prediction of tumor recurrence in patients with breast cancer after NST.<\/jats:p>","DOI":"10.1002\/kjm2.70288","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T06:51:59Z","timestamp":1787813519000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pretreatment Prediction of Tumor Recurrence in Breast Cancer After Neoadjuvant Systemic Therapy Using Machine Learning With Clinical and\n                    <scp>CT<\/scp>\n                    Radiomics Features"],"prefix":"10.1002","author":[{"given":"Huei\u2010Yi","family":"Tsai","sequence":"first","affiliation":[{"name":"Graduate Institute of Clinical Medicine, College of Medicine Kaohsiung Medical University  Kaohsiung Taiwan"},{"name":"Department of Medical Imaging, Kaohsiung Medical University Hospital Kaohsiung Medical University  Kaohsiung Taiwan"},{"name":"Center for Big Data Research Kaohsiung Medical University  Kaohsiung Taiwan"},{"name":"Department of Medical Research Kaohsiung Medical University Hospital  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u2010Ching","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Kaohsiung Medical University Hospital Kaohsiung Medical University  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei\u2010Shiuan","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Kaohsiung Medical University Hospital Kaohsiung Medical University  Kaohsiung Taiwan"},{"name":"Department of Medical Imaging Kaohsiung Municipal Siaogang Hospital  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2953-2727","authenticated-orcid":false,"given":"Jui\u2010Sheng","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Kaohsiung Medical University Hospital Kaohsiung Medical University  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu\u2010Yen","family":"Lin","sequence":"additional","affiliation":[{"name":"Biomedical Artificial Intelligence Academy Kaohsiung Medical University  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming\u2010Feng","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Biomedical Science and Environmental Biology, College of Life Science Kaohsiung Medical University  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8488-2693","authenticated-orcid":false,"given":"Ming\u2010Chung","family":"Chou","sequence":"additional","affiliation":[{"name":"Center for Big Data Research Kaohsiung Medical University  Kaohsiung Taiwan"},{"name":"Department of Medical Research Kaohsiung Medical University Hospital  Kaohsiung Taiwan"},{"name":"Biomedical Artificial Intelligence Academy Kaohsiung Medical University  Kaohsiung Taiwan"},{"name":"Department of Medical Imaging and Radiological Sciences Kaohsiung Medical University  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-025-03502-3"},{"key":"e_1_2_10_3_1","unstructured":"American Cancer Society Survival Rates for Breast Cancer. Accessed August 27 2025(2025) https:\/\/www.cancer.org\/cancer\/types\/breast\u2010cancer\/understanding\u2010a\u2010breast\u2010cancer\u2010diagnosis\/breast\u2010cancer\u2010survival\u2010rates.html."},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0284460"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0163254"},{"key":"e_1_2_10_6_1","first-page":"949","article-title":"Effecst of Patho\u2010 Biological Factors on the Survival of Recurrent Breast Cancer Cases","volume":"19","author":"Akbari M. 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