Predictors of Pneumonitis After Conventionally Fractionated Radiotherapy for Locally Advanced Lung Cancer

Multiple factors influence the risk of developing pneumonitis after radiation therapy (RT) for lung cancer, but few resources exist to guide clinicians in predicting risk in an individual patient treated with modern techniques. We analyzed toxicity data from a state-wide consortium to develop an integrated pneumonitis risk model.

All patients (N = 1302) received conventionally fractionated RT for stage II-III non-small cell lung cancer between April 2012 and July 2019. Pneumonitis occurring within 6 months of treatment was graded by local practitioners and collected prospectively from 27 academic and community clinics participating in a state-wide quality consortium. Pneumonitis was modeled as either grade ≥2 (G2+) or grade ≥3 (G3+). Logistic regression models were fit to quantify univariable associations with dose and clinical factors, and stepwise Akaike information criterion-based modeling was used to build multivariable prediction models.

The overall rate of pneumonitis of any grade in the 6 months following RT was 16% (208 cases). Seven percent of cases (n = 94) were G2+ and <1% (n = 11) were G3+. Adjusting for incomplete follow-up, estimated rates for G2+ and G3+ were 14% and 2%, respectively. In univariate analyses, gEUD, V5, V10, V20, V30, and mean lung dose (MLD) were positively associated with G2+ pneumonitis risk, whereas current smoking status was associated with lower odds of pneumonitis. G2+ pneumonitis risk of ≥22% was independently predicted by MLD of ≥20 Gy, V20 of ≥35%, and V5 of ≥75%. In multivariate analyses, the lung V5 metric remained a significant predictor of G2+ pneumonitis, even when controlling for MLD, despite their close correlation. For G3+ pneumonitis, MLD and V20 were statistically significant predictors. Number of patient comorbidities was an independent predictor of G3+, but not G2+ pneumonitis.

We present an analysis of pneumonitis risk after definitive RT for lung cancer using a large, prospective dataset. We incorporate comorbidity burden, smoking status, and dosimetric parameters in an integrated risk model. These data may guide clinicians in assessing pneumonitis risk in individual patients.