Interpretable multimodal MRI radiomics for predicting neoadjuvant chemotherapy response in nasopharyngeal carcinoma.

ABSTRACT:

Objective

To investigate the value of multimodal MRI radiomics model in predicting Neoadjuvant chemotherapy (NAC) response in Nasopharyngeal carcinoma (NPC) and assess the performance differences between machine learning and deep learning models.

Methods

370 NPC patients were retrospectively enrolled, with 126 patients receiving additional Dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI). Radiomics features were extracted from T1WI, PDWI, CE-T1WI and DCE-MRI sequences. Feature selection was performed through Mutual Information Criterion (MIC), Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, and feature selection network. Machine learning models based on logistic regression, support vector machines, random forests, and extreme gradient boosti

SUBMITTER: Lin TC 

PROVIDER: S-EPMC12752416 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature