P. Stanton

Poor baseline pulmonary function may not increase the risk of radiation-induced lung toxicity

PURPOSE Poor pulmonary function (PF) is often considered a contraindication to definitive radiation therapy for lung cancer. This study investigated whether baseline PF was associated with radiation-induced lung toxicity (RILT) in patients with non-small cell lung cancer (NSCLC) receiving conformal radiation therapy (CRT). METHODS AND MATERIALS NSCLC patients treated with CRT and tested for PF at baseline were eligible. Baseline predicted values of forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), and diffusion capacity of lung for carbon monoxide (DLCO) were analyzed. Additional factors included age, gender, smoking status, Karnofsky performance status, coexisting chronic obstructive pulmonary disease (COPD), tumor location, histology, concurrent chemotherapy, radiation dose, and mean lung dose (MLD) were evaluated for RILT. The primary endpoint was symptomatic RILT (SRILT), including grade ≥2 radiation pneumonitis and fibrosis. RESULTS There was a total of 260 patients, and SRILT occurred in 58 (22.3%) of them. Mean FEV1 values for SRILT and non-SRILT patients were 71.7% and 65.9% (P=.077). Under univariate analysis, risk of SRILT increased with MLD (P=.008), the absence of COPD (P=.047), and FEV1 (P=.077). Age (65 split) and MLD were significantly associated with SRILT in multivariate analysis. The addition of FEV1 and age with the MLD-based model slightly improved the predictability of SRILT (area under curve from 0.63-0.70, P=.088). CONCLUSIONS Poor baseline PF does not increase the risk of SRILT, and combining FEV1, age, and MLD may improve the predictive ability.

Radiation-induced lung toxicity in non-small-cell lung cancer: Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship

BACKGROUND AND PURPOSE Current methods to estimate risk of radiation-induced lung toxicity (RILT) rely on dosimetric parameters. We aimed to improve prognostication by incorporating clinical and cytokine data, and to investigate how these factors may interact with the effect of mean lung dose (MLD) on RILT. MATERIALS AND METHODS Data from 125 patients treated from 2004 to 2013 with definitive radiotherapy for stages I-III NSCLC on four prospective clinical trials were analyzed. Plasma levels of 30 cytokines were measured pretreatment, and at 2 and 4weeks midtreatment. Penalized logistic regression models based on combinations of MLD, clinical factors, and cytokine levels were developed. Cross-validated estimates of log-likelihood and area under the receiver operating characteristic curve (AUC) were used to assess accuracy. RESULTS In prognosticating grade 3 or greater RILT by MLD alone, cross-validated log-likelihood and AUC were -28.2 and 0.637, respectively. Incorporating clinical features and baseline cytokine levels increased log-likelihood to -27.6 and AUC to 0.669. Midtreatment cytokine data did not further increase log-likelihood or AUC. Of the 30 cytokines measured, higher levels of 13 decreased the effect of MLD on RILT, corresponding to a lower odds ratio for RILT per Gy MLD, while higher levels of 4 increased the association. CONCLUSIONS Although the added prognostic benefit from cytokine data in our model was modest, understanding how clinical and biologic factors interact with the MLD-RILT relationship represents a novel framework for understanding and investigating the multiple factors contributing to radiation-induced toxicity.

Prediction of Radiation Esophagitis in Non–Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels

Radiation esophagitis (RE) is a common adverse event associated with radiotherapy for non–small cell lung cancer (NSCLC). While plasma cytokine levels have been correlated with other forms of radiation-induced toxicity, their association with RE has been less well studied. We analyzed data from 126 patients treated on 4 prospective clinical trials. Logistic regression models based on combinations of dosimetric factors [maximum dose to 2 cubic cm (D2cc) and generalized equivalent uniform dose (gEUD)], clinical variables, and pretreatment plasma levels of 30 cytokines were developed. Cross-validated estimates of area under the receiver operating characteristic curve (AUC) and log likelihood were used to assess prediction accuracy. Dose-only models predicted grade 3 RE with AUC values of 0.750 (D2cc) and 0.727 (gEUD). Combining clinical factors with D2cc increased the AUC to 0.779. Incorporating pretreatment cytokine measurements, modeled as direct associations with RE and as potential interactions with the dose-esophagitis association, produced AUC values of 0.758 and 0.773, respectively. D2cc and gEUD correlated with grade 3 RE with odds ratios (ORs) of 1.094/Gy and 1.096/Gy, respectively. Female gender was associated with a higher risk of RE, with ORs of 1.09 and 1.112 in the D2cc and gEUD models, respectively. Older age was associated with decreased risk of RE, with ORs of 0.992/year and 0.991/year in the D2cc and gEUD models, respectively. Combining clinical with dosimetric factors but not pretreatment cytokine levels yielded improved prediction of grade 3 RE compared to prediction by dose alone. Such multifactorial modeling may prove useful in directing radiation treatment planning.