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Purpose <p>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.</p> Materials and methods <p>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 <i>p</i> &lt; 0.05. Second, this set of variables was refined through multicollinearity diagnosis using Spearman correlation and variance inflation factor (VIF) analysis to ensure predictor independence. Third, an optimal multivariate logistic regression model was constructed, incorporating the synthetic minority over-sampling technique (SMOTE) to address class imbalance, and validated using 5-fold cross-validation and an independent test set. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, calibration plots, and the Brier score. A practical risk stratification system was subsequently developed based on the model’s predicted probabilities.</p> Results <p>The overall incidence of SRP was 43.3% (52/120). Univariate analysis identified 12 variables significantly associated with SRP (<i>p</i> &lt; 0.05), encompassing all dose-volume histogram (DVH) parameters (V<sub>5</sub>-V<sub>30</sub>, MLD), target volumes, and other prior therapy (OT). Following multicollinearity analysis, four independent candidates—lung V<sub>5</sub> (%), Lung-to-PTV Ratio (LPR), OT, and treatment planning system (TPS)—were retained for multivariate modeling. Multivariate logistic regression confirmed V<sub>5</sub> (OR = 1.113 per %; <i>p</i> &lt; 0.01), LPR (OR = 0.959 per unit; <i>p</i> &lt; 0.01), and OT (OR = 2.672; <i>p</i> &lt; 0.01) as significant independent predictors, with LPR exhibiting the strongest effect. The final model demonstrated excellent discriminative performance, achieving a cross-validated AUC of 0.875 and a test set AUC of 0.921, along with good calibration (Brier score = 0.13). Based on predicted probabilities, patients were stratified into three distinct risk groups (low: <i>P</i> &lt; 0.3; medium: 0.3 ≤ <i>P</i> ≤ 0.7; high: <i>P</i> &gt; 0.7), enabling effective differentiation of SRP risk for clinical application.</p> Conclusion <p>The integration of a low-dose parameter (V<sub>5</sub>), 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.</p>

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Development of a multifactorial prediction model for symptomatic radiation pneumonitis in lung cancer patients undergoing stereotactic body radiotherapy

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  • 发表日期：

    2026年5月22日

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  • Xiong Yang,
  • Quncai Wang,
  • Changli Ruan,
  • Luzhou Wang,
  • Hongyun Gong,
  • David Huang

摘要

Purpose

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.

Materials and methods

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 < 0.05. Second, this set of variables was refined through multicollinearity diagnosis using Spearman correlation and variance inflation factor (VIF) analysis to ensure predictor independence. Third, an optimal multivariate logistic regression model was constructed, incorporating the synthetic minority over-sampling technique (SMOTE) to address class imbalance, and validated using 5-fold cross-validation and an independent test set. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, calibration plots, and the Brier score. A practical risk stratification system was subsequently developed based on the model’s predicted probabilities.

Results

The overall incidence of SRP was 43.3% (52/120). Univariate analysis identified 12 variables significantly associated with SRP (p < 0.05), encompassing all dose-volume histogram (DVH) parameters (V5-V30, MLD), target volumes, and other prior therapy (OT). Following multicollinearity analysis, four independent candidates—lung V5 (%), Lung-to-PTV Ratio (LPR), OT, and treatment planning system (TPS)—were retained for multivariate modeling. Multivariate logistic regression confirmed V5 (OR = 1.113 per %; p < 0.01), LPR (OR = 0.959 per unit; p < 0.01), and OT (OR = 2.672; p < 0.01) as significant independent predictors, with LPR exhibiting the strongest effect. The final model demonstrated excellent discriminative performance, achieving a cross-validated AUC of 0.875 and a test set AUC of 0.921, along with good calibration (Brier score = 0.13). Based on predicted probabilities, patients were stratified into three distinct risk groups (low: P < 0.3; medium: 0.3 ≤ P ≤ 0.7; high: P > 0.7), enabling effective differentiation of SRP risk for clinical application.

Conclusion

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.