Risk Stratification Model for Resected Squamous-Cell Lung Cancer Patients According to Clinical and Pathological
Sara Pilotto1, Isabella Sperduti2, Silvia Novello3
1Department of Medical Oncology, University of Verona, Azienda Ospedaliera Universitaria Integrata, Verona, Italy.
Introduction:
The aim of this analysis (AIRC-MFAG project no. 14282) was to define a risk classification for resected squamous-cell lung cancer based on the combination of clinicopathological predictors to provide a practical tool to evaluate patients' prognosis.
Methods:
Clinicopathological data were retrospectively correlated to disease-free/cancer-specific/overall survival (DFS/CSS/OS) using a Cox model. Individual patient probability was estimated by logistic equation. A continuous score to identify risk classes was derived according to model ratios and dichotomized according to prognosis with receiver operating characteristic analysis.
Results:
Data from 573 patients from five institutions were gathered. Four hundred ninety-four patients were evaluable for clinical analysis (median age: 68 years; male/female: 403/91; T-descriptor according to TNM 7th edition 1-2/3-4: 330/164; nodes 0/>0: 339/155; stages I and II/III and IV: 357/137). At multivariate analysis, age, T-descriptor according to TNM 7th edition, nodes, and grading were independent predictors for DFS and OS; the same factors, except age and grading, predicted CSS. Multivariate model predict individual patient probability with high prognostic accuracy (0.67 for DFS). On the basis of receiver operating characteristic-derived cutoff, a two-class model significantly differentiated low-risk and high-risk patients for 3-year DFS (64.6% and 32.4%, p < 0.0001), CSS (84.4% and 44.5%, p < 0.0001), and OS (77.3% and 38.8%, p < 0.0001). A three-class model separated low-risk, intermediate-risk, and high-risk patients for 3-year DFS (64.6%, 39.8%, and 21.8%, p < 0.0001), CSS (84.4%, 55.4%, and 30.9%, p< 0.0001), and OS (77.3%, 47.9%, and 27.2%, p < 0.0001).
Conclusions:
A risk stratification model including often adopted clinicopathological parameters accurately separates resected squamous-cell lung cancer patients into different risk classes. The project is currently ongoing to integrate the clinicopathological model with investigational molecular predictors.
