Radiotherapy dosage: A neural network approach for uninvolved liver dose in stereotactic body radiation therapy for liver cancer.

Abstract

A recent study by Zhang et al developed a neural network-based predictive model for estimating doses to the uninvolved liver during stereotactic body radiation therapy (SBRT) in liver cancer. The study reported a significant advancement in personalized radiotherapy by improving accuracy and reducing treatment-related toxicity. The model demonstrated strong predictive performance with R-values above 0.8, indicating its potential to improve treatment consistency. However, concerns arise from the small sample size and exclusion criteria, which may limit generalizability. Future studies should incorporate larger, more diverse patient cohorts, explore potential confounding factors such as tumor characteristics and delivery technique variability, and address the long-term effects of SBRT.