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Express"],"published-print":{"date-parts":[[2026,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The integration of artificial intelligence (AI) into breast cancer management presents transformative potential for both diagnosis and treatment planning. This study introduces a resilient AI framework designed to accomplish, from breast MRI images, two critical tasks: (1) accurate and automated segmentation of breast tumors, and (2) T-stage classification of breast cancer in accordance with the 2018 eighth edition of TNM staging system.The dataset comprises sagittal MRI scans utilized for tumor segmentation through a U-Net architecture, which yielded high precision and specificity.Segmented images were utilized as input to a ResNet-50 convolutional neural network, which demonstrated robust classification performance across all T stage categories (T1mi, T1a, T1b, T1c, T2, T3, T4a, T4b, T4c, and T4d), highlighting its high precision, specificity, and F1-scores in accurately distinguishing tumor progression.The T-stage serves as a critical determinant in selecting appropriate treatment modalities, ranging from surgery and chemotherapy to radiotherapy or palliative care, and in estimating prognosis. Our classification results underscore the clinical significance of tumor size progression in early stages (T1mi\u2013T2), where each incremental increase in diameter is associated with poorer outcomes. For advanced categories (T3\u2013T4a\u2013T4d), our model consistently highlighted a uniformly poor prognosis, irrespective of tumor dimensions, reinforcing the pivotal role of anatomical invasion in staging and therapeutic decisions. This AI framework represents a significant advancement in breast cancer automation, enabling more precise staging and fostering improved clinical decision-making and patient outcomes.<\/jats:p>","DOI":"10.1088\/2057-1976\/ae4df0","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T22:47:47Z","timestamp":1772750867000},"page":"025035","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial intelligence for T classification of TNM breast cancer in MRI imaging: enabling precision in treatment decisions"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6718-492X","authenticated-orcid":true,"given":"Hamida","family":"Romdhane","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dorra","family":"Ben-Sellem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"bpexae4df0bib1","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1200\/CCI.20.00110","type":"journal-article","article-title":"Reimagining T staging through artificial intelligence and machine learning image processing approaches in digital pathology","volume":"4","author":"Bera","year":"2020","journal-title":"JCO Clin Cancer Inform"},{"key":"bpexae4df0bib2","doi-asserted-by":"publisher","first-page":"5216","DOI":"10.3390\/cancers15215216","type":"journal-article","article-title":"Impact of imaging biomarkers and AI on breast cancer management: a brief review","volume":"15","author":"Saleh","year":"2023","journal-title":"Cancers"},{"key":"bpexae4df0bib3","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s13058-023-01687-4","type":"journal-article","article-title":"Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review","volume":"25","author":"Adam","year":"2023","journal-title":"Breast Cancer Res"},{"key":"bpexae4df0bib4","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.1007\/s11042-024-20271-2","type":"journal-article","article-title":"Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities","volume":"84","author":"Karthiga","year":"2025","journal-title":"Multimed Tools Appl"},{"key":"bpexae4df0bib5","doi-asserted-by":"crossref","DOI":"10.1109\/ICHMS65439.2025.11154329","type":"preprint","article-title":"Integrating AI for human-centric breast cancer diagnostics: a multi-scale and multi-view swin transformer framework","author":"Bayatmakou","year":"2025"},{"key":"bpexae4df0bib6","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.3390\/diagnostics15131681","type":"journal-article","article-title":"Deep learning-enhanced T1-weighted imaging for breast MRI at 1.5T","volume":"15","author":"Olthof","year":"2025","journal-title":"Diagnostics"},{"key":"bpexae4df0bib7","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s11547-023-01594-w","type":"journal-article","article-title":"Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features","volume":"128","author":"Zhang","year":"2023","journal-title":"Radiol med"},{"key":"bpexae4df0bib8","doi-asserted-by":"publisher","first-page":"7116","DOI":"10.3390\/ijms24087116","type":"journal-article","article-title":"The variety of 3D breast cancer models for the study of tumor physiology and drug screening","volume":"24","author":"Fr\u00f6hlich","year":"2023","journal-title":"Int. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-01-15","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-03-05","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-03-13","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}