Risk Stratification of Locally Advanced Non-Small Cell Lung Cancer (NSCLC) Patients Treated with Chemo-Radiotherapy:...

The purpose of this study was to determine which factors predicted survival and to derive a risk prediction model for patients with locally advanced non-small cell lung cancer (NSCLC) receiving concurrent chemo-radiotherapy (cCRT).

This investigation included 149 patients with locally advanced NSCLC who were treated with cCRT at Stony Brook University Hospital between 2007 and 2015. A finite set of demographic, clinical, and treatment variables were evaluated as independent prognostic factors. Kaplan-Meier survival curves were generated, and log rank tests were used to evaluate difference in survival between groups. To derive a risk score for mortality, a machine learning approach was utilized. To maximize statistical power while examining replicability, the sample was split into discovery (n=99) and replication (n=50) subsamples. Elastic-net regression was used to identify a linear prediction model. Youden's index was used to identify appropriate cutoffs. Cox proportional hazards regression was used to examine mortality risk; model concordance and hazards ratios were reported.

One-quarter of the patients survived for three years after initiation of cCRT. Prognostic factors for survival in the discovery group included age, sex, smoking status, albumin, histology, largest tumor size, number of nodal stations, stage, induction therapy, and radiation dose. The derived model had good risk predictive accuracy (C=0.70). Median survival time was shorter in the high-risk group (0.93 years) vs the low-risk group (2.40 years). Similar findings were noted in the replication sample with strong model accuracy (C=0.69) and median survival time of 0.93 years and 2.03 years for the high- and low-risk groups, respectively.

This novel risk prediction model for overall survival in patients with stage III NSCLC highlights the importance of integrating patient, clinical, and treatment variables for accurately predicting outcomes. Clinicians can use this tool to make personalized treatment decisions for patients with locally advanced NSCLC treated with concurrent chemo-radiation.