Impact of normal lung volume choices on radiation pneumonitis risk prediction in locally advanced NSCLC radiotherapy

TL;DR

This study investigates how different lung volume definitions affect the prediction of radiation pneumonitis risk in NSCLC radiotherapy, finding that excluding PTV from lung volume improves predictive accuracy.

Contribution

It introduces a comparison of lung volume definitions in RP risk prediction and demonstrates that excluding PTV enhances machine learning model performance.

Findings

V20 and MLD are significant RP predictors.

Excluding PTV from lung volume improves ML prediction performance.

SHAP analysis highlights V20 and MLD as key features.

Abstract

This study is to evaluate the impact of lung volume choices on predicting radiation pneumonitis (RP) risk in patients with locally advanced NSCLC undergoing radiotherapy. Dosimetric variables V20, V5, and mean lung dose (MLD) were extracted from the treatment plans of 442 patients enrolled in the NRG Oncology RTOG 0617 trial. Three lung volumes were defined: total lung excluding gross-tumor-target (TL-GTV), total lung excluding clinical-target-volume (TL-CTV), and total lung excluding planning-target-volume (TL-PTV). Patients were grouped as no-RP2 (N = 377, grade <= 1 RP) and RP2 (N = 65, grade >= 2 RP). Statistical analyses were performed to assess the effect on lung volume definition on RP2 prediction. Three supervised machine learning (ML) models: logistic regression (LR), k-Nearest Neighbor (kNN), and eXtreme Gradient Boosting (XGB), were used to evaluate predictive performance. Model performance was quantified using the area under the receiver operating characteristic curve (AUC), and statistical significance was tested via a bootstrap analysis. Shapley Additive Explanations (SHAP) were applied to interpret feature contributions to model predictions. Statistical analyses showed that V20 and MLD were significantly associated with RP2, while differences among volume definitions were not statistically significant. Both kNN and XGB classifiers consistently yielded higher AUC values for the TL-PTV definition compared to the other definitions, a finding supported by bootstrap analysis. SHAP analysis further indicated that V20 and MLD were the most influential predictors of RP2. Both statistical analysis and SHAP confirmed that V20 and MLD were associated with RP2. The ML models indicated that defining normal lung volume as total lung excluding PTV yielded the highest predictive performance for RP2 risk. Further validation using external datasets is warranted to confirm these findings.