Neoadjuvant Chemotherapy

Neoadjuvant chemotherapy (NAC) aims to shrink tumors before surgery, improving treatment outcomes for various cancers. Current research heavily focuses on developing accurate predictive models to identify patients who will benefit most from NAC, using machine learning techniques like deep learning (including U-Net architectures and XGBoost) and Bayesian Networks, often incorporating multiparametric MRI, diffusion-weighted imaging, and radiomics features extracted from medical images. These efforts leverage large datasets and advanced algorithms to improve the precision of treatment selection, potentially reducing unnecessary side effects and optimizing patient care. The ultimate goal is to personalize NAC treatment based on individual patient characteristics and tumor response prediction, leading to more effective and targeted cancer therapies.