BackgroundEarly 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−response by integrating clinicopathological, tumor microenvironment (TME), longitudinal magnetic resonance imaging (MRI), and systemic inflammatory features.MethodsIn this retrospective study, 112 patients with primary breast cancer underwent baseline MRI, a second MRI after two NAT cycles, and definitive surgery.
Non−response was defined as Miller–Payne grades 1–3. Candidate predictors were categorized into four domains.
After univariate screening, domain−specific 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−validation and 500−iteration bootstrap.
Calibration and decision curve analyses were also performed.ResultsThirty−eight patients (33.9%) were non−responders. The individual domain models achieved apparent AUCs of 0.844 (clinical), 0.786 (imaging), 0.828 (TME), and 0.706 (inflammatory).
Frontiers in Immunology published a clinical update in Infectious Disease on 10 Jun 2026.
The item focuses on Early identification of neoadjuvant therapy non-response via multimodal immune-imaging biomarkers in breast cancer.
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