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To address these challenges and enhance clinical applicability, we propose a Multi-Level Volumetric Transformer (MVT-Former), a novel model that predicts NAC response directly from non-segmented, full-field breast MRI data combined with relevant clinical information. The primary novelty of this work lies in its specialized dual-transformer design: (1) the Multi-Level Convolutional Spatial Transformer (MLCS-Former), which utilizes multi-scale convolutions and a Global Convolutional Attention (GCA) mechanism to extract fine-grained textural and morphological features from 2D MRI slices without manual annotations; and (2) the Volume Feature Learning Transformer (VFL-Former), which captures 3D structural changes and long-range dependencies across the entire MRI volume. We evaluated the MVT-Former on the I-SPY-1 TRIAL dataset and results demonstrate that the proposed model outperforms state-of-the-art methods, achieving superior performance across key metrics, including area under the curve, accuracy, sensitivity, and specificity.<\/jats:p>","DOI":"10.1145\/3793536","type":"journal-article","created":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T11:25:28Z","timestamp":1769253928000},"page":"1-15","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Level Volumetric Transformer for Early Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-4962-882X","authenticated-orcid":false,"given":"Monu","family":"Verma","sequence":"first","affiliation":[{"name":"Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, Indiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-0489-3566","authenticated-orcid":false,"given":"Fernando","family":"Collado-Mesa","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-4163-7230","authenticated-orcid":false,"given":"Mohamed","family":"Abdel-Mottaleb","sequence":"additional","affiliation":[{"name":"Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, Indiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Nathaniel Braman Mohammed El Adoui Manasa Vulchi Paulette Turk Maryam Etesami Pingfu Fu Kaustav Bera Stylianos Drisis Vinay Varadan Donna Plecha et al. 2020. 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