Sara Pilotto1, Isabella Sperduti2, Silvia Novello3, Umberto Peretti1, Michele Milella4, Francesco Facciolo5, Sabrina Vari4, Giovanni Leuzzi5, Tiziana Vavalà3, Antonio Marchetti6, Felice Mucilli6, Lucio Crinò7, Francesco Puma7, Stefania Kinspergher1, Antonio Santo1, Luisa Carbognin1, Matteo Brunelli8, Marco Chilosi8, Aldo Scarpa9, Giampaolo Tortora1, Emiolio Bria10. 1. Department of Medical Oncology, University of Verona, Azienda Ospedaliera Universitaria Integrata, Verona, Italy. 2. Biostatistics, Regina Elena National Cancer Institute, Rome, Italy. 3. Department of Oncology, University of Torino, A.O.U. San Luigi, Orbassano (Torino), Italy. 4. Department of Medical Oncology, Regina Elena National Cancer Institute, Rome, Italy. 5. Department of Thoracic Surgery, Regina Elena National Cancer Institute, Rome, Italy. 6. Department of Pathology, Clinical Research Center, Center of Excellence on Aging, University-Foundation, Chieti, Italy. 7. Department of Medical Oncology, University of Perugia, Perugia, Italy. 8. Department of Pathology and Diagnostics, University of Verona, Verona, Italy. 9. Department of Pathology and Diagnostics, University of Verona, Verona, Italy; Department of Pathology and Diagnostics, ARC-NET Applied Research on Cancer Center, University of Verona, Verona, Italy. 10. Department of Medical Oncology, University of Verona, Azienda Ospedaliera Universitaria Integrata, Verona, Italy. Electronic address: emilio.bria@univr.it.
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.
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 cancerpatients into different risk classes. The project is currently ongoing to integrate the clinicopathological model with investigational molecular predictors.