Symptomatic radiation pneumonitis (SRP) remains a clinically significant toxicity following lung stereotactic body radiotherapy (SBRT). However, existing prediction models have seen limited clinical adoption, as they tend to be either too complex for routine application or too simplistic for reliable risk stratification. This study aimed to develop a robust prediction model by integrating key dosimetric, volumetric, and clinical variables, and to translate this model into a practical risk score to support individualized pretreatment assessment. This retrospective analysis included 120 patients with primary lung cancer treated with SBRT between January 2019 and December 2024. A total of 24 demographic, clinical, volumetric, and dosimetric parameters were evaluated. The primary endpoint was the development of SRP. A three-step statistical framework was employed for model development. First, candidate predictors were identified using univariate analysis (Mann-Whitney U test for continuous variables and Chi-square test for categorical variables), with statistical significance defined as p 0.7), enabling effective differentiation of SRP risk for clinical application. The integration of a low-dose parameter (V5), a novel volumetric ratio (LPR), and other prior treatment history (OT) yields a robust and parsimonious model for predicting SRP after lung SBRT. The derived three-tiered risk score offers a practical, interpretable tool that may inform individualized patient counseling and assist clinicians during treatment planning, although further prospective validation is needed.
Yang et al. (Fri,) studied this question.