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.

Keywords: Liver cancer; Radiotherapy dosage; Dose prediction; Machine learning; Stereotactic body radiotherapy

Core Tip: A study by Zhang et al developed a neural network-based predictive model for estimating doses to uninvolved liver tissue during stereotactic body radiation therapy (RT), representing a significant advancement in personalizing RT for liver cancer patients. The model demonstrated high predictive accuracy, with R-values exceeding 0.8, highlighting its potential to standardize dose estimation and improve patient safety by reducing biases. The study's relatively small patient cohort (114 patients) raises concerns about selection bias and limits the model's generalizability. Future research should involve larger multicenter cohorts and a more comprehensive cohort of patient characteristics to improve the generalizability of models and clinical relevance. Interdisciplinary collaboration among oncologists, data scientists, and radiation technologists is vital for improving predictive models and the efficacy and precision of cancer treatment.