Purpose: To identify factors that may predict for severe radiation pneumonitis or pneumonopathy (RP), we reviewed a set of simple, commonly available characteristics.
Methods and materials: Medical records of 148 lung cancer patients with good performance status (ECOG 0-1) treated definitively with chemoradiation from 6/92-6/98 at the University of Pennsylvania were reviewed. Actuarial survival and the crude rate of severe radiation pneumonitis were determined as a function of several variables. Potential predictive factors examined included age, gender, histology, stage, pulmonary function, performance status (0 vs. 1), weight loss, tumor location, radiation dose, initial radiation field size, chemotherapy regimen, and timing of chemotherapy. Univariate analysis (log-rank test) was performed for each variable. Multivariate analysis was performed using linear regression.
Results: Median survival for the entire cohort was 14.7 months. Four patients were inevaluable for pneumonitis due to early death from progressive disease. Of the remaining 144 evaluable patients, 12 (8.3%) experienced severe RP. The most significant factor predicting for severe RP was performance status (p < 0.003). The risk of severe RP was 16% for PS-1 patients vs. 2% for PS-0 patients. Women were significantly more likely to develop severe RP than men (p = 0.01). Among 67 patients for whom pre-radiation therapy pulmonary function data were available, forced expiratory volume of the lung in 1 second (FEV(1)) was also significant (p = 0. 03). No patient suffering severe RP had a pretreatment FEV(1) > 2.0 liters. The median radiation dose was 59.2 Gy and median initial radiation field size was 228 cm(2). Neither radiotherapy factor predicted for RP. Other factors studied, including chemotherapy drugs, and schedule, also were not significant predictors of severe RP.
Conclusions: Pretreatment performance status, gender, and FEV(1) are significant predictors of severe radiation pneumonopathy, at least when using conventional radiation fields and doses. Complex radiation dose-volume algorithms that attempt to predict lung complication probabilities should probably incorporate these simply obtained clinical parameters.