ArticleFrontiers in medicine2025
Personalized predictions of neoadjuvant chemotherapy response in breast cancer using machine learning and full-field digital mammography radiomics.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to develop a comprehensive nomogram model by integrating clinical pathological and full-field digital mammography (FFDM) radiomic features to predict the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer patients, thereby providing personalized treatment recommendations. Methods: A retrospective analysis was conducted on the clinical and imaging data of 227 breast cancer patients from 2016 to 2024 at the Second Affiliated Hospital of Harbin Medical University. The patients were divided into a training set ( Results: The radiomics model based on support vector machine (SVM) demonstrated the best predictive performance. FFDM tumor density and HER-2 status were identified as independent risk factors for achieving pathologic complete response (PCR) after NAC ( Conclusion: The nomogram prediction model, developed by integrating clinical pathological and radiomic features, demonstrated significant performance in predicting the efficacy of NAC in breast cancer, providing valuable reference for clinical personalized prediction planning.
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