PurposeAlthough patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient.Methods and MaterialsData from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130).ResultsThe final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient’s survival probability (www.predictcancer.org). The data set can be downloaded at https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048.ConclusionsThe prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.

Introduction Patients treated with stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) are subject to locoregional and distant recurrence, as well as the formation of second primary lung cancers (SPLC). The optimal surveillance regimen for patients treated with SBRT for early-stage NSCLC remains unclear, and herein we investigated the post-treatment recurrence patterns and development of SPLC. Methods Three-hundred and sixty-six patients with pathologically proven inoperable early-stage NSCLC treated with SBRT between 2006 and 2013 were assessed. Patients underwent a CT scan of the chest every 3 months during years 1 and 2, every 6 months during years 3 and 4, and annually thereafter. Competing risks analysis was used for all time-to-event analyses. Results With a median follow up of 23 months, the 2-year cumulative incidence of local, nodal and distant failures were 12.2%, 16.1%, and 15.5%, respectively. Of patients with disease progression post-SBRT (n=108), 84% (n=91) occurred within the first two years. Five percent (n=19) of patients developed a SPLC. The median time to development of SPLC was 16.5 months (range 6.5 to 71.1 months), with 33% (n=6) of these patients developing SPLCs after two years. None of the never smokers, but 4% of former and 15% of current tobacco smokers developed a SPLC (p=0.005). Conclusion Close monitoring with routine CT scans within the first 2 years after SBRT is effective in detecting early disease progression. In contrast, the risk for developing a SPLC remains elevated beyond 2 years, particularly in former and current smokers.

Objectives Glucose metabolic activity measured by [18F]-fluoro-2-deoxy-glucose positron emission tomography (FDG-PET) has shown prognostic value in multiple malignancies, but results are often confounded by the inclusion of patients with various disease stages and undergoing various therapies. This study was designed to evaluate the prognostic value of tumor FDG uptake quantified by maximum standardized uptake value (SUVmax) in a large group of early-stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT) using consistent treatment techniques. Materials and Methods 219 lesions in 211 patients treated with definitive SBRT for stage I NSCLC were analyzed after a median follow-up of 25.2 months. Cox regression was used to determine associations between SUVmax and overall survival (OS), disease-specific survival (DSS), and freedom from local recurrence (FFLR) or distant metastasis (FFDM). Results SUVmax >3.0 was associated with worse OS (p<0.001), FFLR (p=0.003) and FFDM (p=0.003). On multivariate analysis, OS was associated with SUVmax (HR 1.89, p=0.03), gross tumor volume (GTV) (HR 1.94, p=0.005) and Karnofsky performance status (KPS) (HR 0.51, p=0.008). DSS was associated only with SUVmax (HR 2.58, p=0.04). Both LR (HR 11.47, p=0.02) and DM (HR 3.75, p=0.006) were also associated with higher SUVmax. Conclusion In a large patient population, SUVmax >3.0 was associated with worse survival and a greater propensity for local recurrence and distant metastasis after SBRT for NSCLC.

SBRT compared to CONV is associated with improved LF rates and OS. Our data supports the continued use and expansion of SBRT as the standard of care treatment for inoperable early-stage NSCLC.

Purpose To determine the role of patient/tumor characteristics, radiation dose, and fractionation using the linear-quadratic (LQ) model to predict stereotactic body radiation therapy–induced grade ≥2 chest wall pain (CWP2) in a larger series and develop clinically useful constraints for patients treated with different fraction numbers. Methods and Materials A total of 316 lung tumors in 295 patients were treated with stereotactic body radiation therapy in 3 to 5 fractions to 39 to 60 Gy. Absolute dose–absolute volume chest wall (CW) histograms were acquired. The raw dose-volume histograms (α/β = ∞ Gy) were converted via the LQ model to equivalent doses in 2-Gy fractions (normalized total dose, NTD) with α/β from 0 to 25 Gy in 0.1-Gy steps. The Cox proportional hazards (CPH) model was used in univariate and multivariate models to identify and assess CWP2 exposed to a given physical and NTD. Results The median follow-up was 15.4 months, and the median time to development of CWP2 was 7.4 months. On a univariate CPH model, prescription dose, prescription dose per fraction, number of fractions, D83cc, distance of tumor to CW, and body mass index were all statistically significant for the development of CWP2. Linear-quadratic correction improved the CPH model significance over the physical dose. The best-fit α/β was 2.1 Gy, and the physical dose (α/β = ∞ Gy) was outside the upper 95% confidence limit. With α/β = 2.1 Gy, VNTD99Gy was most significant, with median VNTD99Gy = 31.5 cm3 (hazard ratio 3.87, P<.001). Conclusion There were several predictive factors for the development of CWP2. The LQ-adjusted doses using the best-fit α/β = 2.1 Gy is a better predictor of CWP2 than the physical dose. To aid dosimetrists, we have calculated the physical dose equivalent corresponding to VNTD99Gy = 31.5 cm3 for the 3- to 5-fraction groups.