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However, reliable pre-treatment predictors based on routinely available clinical data remain limited. This study evaluated whether clinicopathologic and baseline magnetic resonance imaging (MRI) variables could predict NAC response using classical machine learning (ML) approaches. Methods: A retrospective cohort of 112 patients with HER2-positive BC treated with NAC was analysed, including 57 patients who achieved pCR and 55 who did not. Fourteen pre-treatment variables were evaluated, including hormone receptor (HR) status, tumour grade, ER and PR expression, Ki67, nodal status, age, and baseline MRI characteristics. Seventy ML classifiers were compared using a fully nested leave-one-out cross-validation (LOOCV) framework. Performance was assessed using accuracy, area under the receiver operating characteristic curve (AUROC), Cohen\u2019s kappa, sensitivity, specificity, and F1-score. Results: A support vector machine (SVM) with a radial basis function (RBF) kernel achieved the highest observed accuracy (82.1%) among the 70 evaluated classifiers. The corresponding AUROC was 83.3%, Cohen\u2019s kappa was 64.2%, and sensitivity, specificity, and F1-score were 87.7%, 76.4%, and 83.3%, respectively. HR-related variables, particularly PR and ER expression, together with Ki67 and baseline MRI features, ranked among the most influential predictors. Despite relying exclusively on routinely available pre-treatment variables, the model demonstrated meaningful predictive performance. Conclusions: ML applied to routinely available clinicopathologic and baseline MRI variables showed promising ability to predict pCR before treatment initiation in HER2-positive BC. The proposed approach may support pre-treatment clinical risk assessment using information already generated during routine clinical assessment. Nevertheless, prospective multicentre external validation, calibration assessment, and evaluation of clinical utility are required before implementation in routine clinical practice.<\/jats:p>","DOI":"10.3390\/diagnostics16152374","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T11:45:26Z","timestamp":1785239126000},"page":"2374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Practical Machine Learning Model for Predicting Neoadjuvant Response in HER2-Positive Breast Cancer"],"prefix":"10.3390","volume":"16","author":[{"given":"Mar\u00eda","family":"Azmat","sequence":"first","affiliation":[{"name":"Centro Singular de Investigaci\u00f3n en Tecnolox\u00edas Intelixentes da USC (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luc\u00eda","family":"Gra\u00f1a-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Radiology Department, Hospital Lucus Augusti Lugo, 27003 Lugo, Spain"},{"name":"Breast Pathology Group, Hospital Universitario Lucus Augusti, 27003 Lugo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5483-9424","authenticated-orcid":false,"given":"Manuel","family":"Fern\u00e1ndez-Delgado","sequence":"additional","affiliation":[{"name":"Centro Singular de Investigaci\u00f3n en Tecnolox\u00edas Intelixentes da USC (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1562-2553","authenticated-orcid":false,"given":"Eva","family":"Cernadas","sequence":"additional","affiliation":[{"name":"Centro Singular de Investigaci\u00f3n en Tecnolox\u00edas Intelixentes da USC (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1258-3517","authenticated-orcid":false,"given":"Marcelino","family":"Maneiro","sequence":"additional","affiliation":[{"name":"Institute for Research in Global Health and Sustainable Development, iTERRA, Inorganic Chemistry Department, Faculty of Sciences, Campus Terra, University of Santiago de Compostela, 27002 Lugo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"N\u00fa\u00f1ez","sequence":"additional","affiliation":[{"name":"Institute for Research in Global Health and Sustainable Development, iTERRA, Inorganic Chemistry Department, Faculty of Sciences, Campus Terra, University of Santiago de Compostela, 27002 Lugo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1016\/S0140-6736(20)32381-3","article-title":"Breast cancer","volume":"397","author":"Loibl","year":"2021","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/S0140-6736(13)62422-8","article-title":"Pathological complete response and long-term clinical benefit in breast cancer: The CTNeoBC pooled analysis","volume":"384","author":"Cortazar","year":"2014","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/S1470-2045(11)70336-9","article-title":"Efficacy and safety of neoadjuvant pertuzumab and trastuzumab in women with locally advanced, inflammatory, or early HER2-positive breast cancer (NeoSphere): A randomised multicentre, open-label, phase 2 trial","volume":"13","author":"Gianni","year":"2012","journal-title":"Lancet Oncol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1093\/annonc\/mdt182","article-title":"Pertuzumab plus trastuzumab in combination with standard neoadjuvant anthracycline-containing and anthracycline-free chemotherapy regimens in patients with HER2-positive early breast cancer: A randomized phase II cardiac safety study (TRYPHAENA)","volume":"24","author":"Schneeweiss","year":"2013","journal-title":"Ann. 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