Using Machine Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Local Advanced Rectal Cancer Based on Texture Features of MRI

Motivation: To explore and validate the association between magnetic resonance image texture features and the efficacy of Neoadjuvant Chemoradiotherapy for rectal cancer. Goal(s): To predict the efficacy of neoadjuvant chemoradiotherapy for locally advanced rectal cancer using machine learning methods. Approach: The wavelet texture parameters of all lesions in patients' MRI images were extracted, and feature selection was performed using random forest classifier model, and then classification learning was performed using the XGBoost classifier. Results: The model based on wavelet texture feature analysis of MRI can effectively predict the effect of neoadjuvant radiochemotherapy for rectal cancer patients. Impact: Through this study, we explored and validated the relationship between MRI texture features and the efficacy of Neoadjuvant Chemoradiotherapy for rectal cancer, providing new guidance and decision support for individualized treatment strategies.