{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T06:00:32Z","timestamp":1781071232930,"version":"3.54.1"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early identification of breast cancer patients unlikely to benefit from neoadjuvant therapy (NAT) remains a critical unmet need. This study aimed to develop and internally validate a multimodal prediction model for NAT non\u2212response by integrating clinicopathological, tumor microenvironment (TME), longitudinal magnetic resonance imaging (MRI), and systemic inflammatory features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective study, 112 patients with primary breast cancer underwent baseline MRI, a second MRI after two NAT cycles, and definitive surgery. Non\u2212response was defined as Miller\u2013Payne grades 1\u20133. Candidate predictors were categorized into four domains. After univariate screening, domain\u2212specific multivariable logistic regression was performed, and retained variables entered least absolute shrinkage and selection operator (LASSO) regression to construct a final multimodal model. Internal validation included five-fold cross\u2212validation and 500\u2212iteration bootstrap. Calibration and decision curve analyses were also performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Thirty\u2212eight patients (33.9%) were non\u2212responders. The individual domain models achieved apparent AUCs of 0.844 (clinical), 0.786 (imaging), 0.828 (TME), and 0.706 (inflammatory). Following LASSO selection, nine features were retained: HER2 status, ER status, Ki\u221267 index, late enhancement rate after two cycles (LER2), baseline background parenchymal enhancement (BPE), time to peak after two cycles (TTP2), tumor\u2212stroma ratio (TSR), tumor\u2212infiltrating lymphocytes (TILs), and pan\u2212immune\u2212inflammation value after two cycles (PIV2). The multimodal model yielded an apparent AUC of 0.933 (95% CI: 0.890\u20130.977), with a bootstrap\u2212corrected AUC of 0.855 and a mean five-fold cross\u2212validation AUC of 0.908 \u00b1 0.038. TILs, TSR, PIV2, and Ki\u221267 were independent predictors. The model demonstrated acceptable calibration after correction for optimism and a net clinical benefit across a range of thresholds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A multimodal prediction model integrating clinicopathological, imaging, tumor microenvironment, and systemic inflammatory features showed potential for early identification of breast cancer patients unlikely to benefit from neoadjuvant therapy. However, given the limited sample size and exploratory single-center design, performance estimates should be interpreted cautiously, and external validation is essential.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1877547","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T05:20:31Z","timestamp":1781068831000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Early identification of neoadjuvant therapy non-response via multimodal immune-imaging biomarkers in breast cancer"],"prefix":"10.3389","volume":"17","author":[{"given":"Xiangyuan","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Radiology, Jiangxi Cancer Hospital & Institute, Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College","place":["Nanchang, Jiangxi, 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