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.
Pilotto, S., Sperduti, I., Novello, S., Peretti, U., Milella, M., Facciolo, F., Vari, S., Leuzzi, G., Vavalà, T., Marchetti, A., Mucilli, F., Crinò, L., Puma, F., Kinspergher, S., Santo, A., Carbognin, L., Brunelli, M., Chilosi, M., Scarpa, A., Tortora, G., Bria, E., Risk Stratification Model for Resected Squamous-Cell Lung Cancer Patients According to Clinical and Pathological Factors, <<JOURNAL OF THORACIC ONCOLOGY>>, 2015; 10 (9): 1341-1348. [doi:10.1097/JTO.0000000000000628] [http://hdl.handle.net/10807/122991]
Risk Stratification Model for Resected Squamous-Cell Lung Cancer Patients According to Clinical and Pathological Factors
Leuzzi, Giovanni;Tortora, Giampaolo;Bria, Emilio
2015
Abstract
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.
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