From dosimetry to deep learning: personalized risk prediction models for radiation pneumonitis

Abstract Radiation pneumonitis (RP) is a common complication following radiotherapy for thoracic tumors, significantly impacting treatment efficacy and patient quality of life. Identifying and predicting risk factors for RP has become a key research focus. This study aims to summarize current knowledge by analyzing previously published studies and large clinical trials. A systematic literature search was conducted in Embase, PubMed, and Web of Science for publications up to November 1, 2024, using keywords such as “radiation pneumonitis”, “risk factors”, “machine learning”, etc. Inclusion criteria prioritized clinical relevance and methodological rigor. Identified RP-related factors include radiation dose parameters (e.g., V20, mean lung dose [MLD]), clinical characteristics (e.g., age, interstitial lung disease), inflammatory markers (e.g., IL-6, neutrophil-to-lymphocyte ratio [NLR]), and features from imaging and multi-omics analyses. In addition, traditional dosimetric indicators remain central, while recent advances integrate radiomics and artificial intelligence (AI)-driven models to improve predictive accuracy. Despite progress, challenges such as limited sample sizes, lack of standardization, and insufficient multi-center validation persist. Future efforts should prioritize data integration, model optimization, and clinical translation to better predict RP risk and guide individualized interventions.