TY - JOUR
T1 - Risk Stratification Model for Resected Squamous-Cell Lung Cancer Patients According to Clinical and Pathological Factors
AU - Pilotto, Sara
AU - Sperduti, Isabella
AU - Novello, Silvia
AU - Peretti, Umberto
AU - Milella, Michele
AU - Facciolo, Francesco
AU - Vari, Sabrina
AU - Leuzzi, Giovanni
AU - Leuzzi, Giovanni Rosario
AU - Vavalà, Tiziana
AU - Marchetti, Antonio
AU - Mucilli, Felice
AU - Crinò, Lucio
AU - Puma, Francesco
AU - Kinspergher, Stefania
AU - Santo, Antonio
AU - Carbognin, Luisa
AU - Brunelli, Matteo
AU - Chilosi, Marco
AU - Scarpa, Aldo
AU - Tortora, Giampaolo
AU - Bria, Emilio
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Aged
KW - Carcinoma, Non-Small-Cell Lung
KW - Carcinoma, Squamous Cell
KW - Clinicopathological predictors
KW - Female
KW - Humans
KW - Male
KW - Oncology
KW - Prognosis
KW - Prognostic model
KW - Pulmonary and Respiratory Medicine
KW - Risk Factors
KW - Squamous lung cancer
KW - Survival Analysis
KW - Aged
KW - Carcinoma, Non-Small-Cell Lung
KW - Carcinoma, Squamous Cell
KW - Clinicopathological predictors
KW - Female
KW - Humans
KW - Male
KW - Oncology
KW - Prognosis
KW - Prognostic model
KW - Pulmonary and Respiratory Medicine
KW - Risk Factors
KW - Squamous lung cancer
KW - Survival Analysis
UR - http://hdl.handle.net/10807/122991
UR - https://www.journals.elsevier.com/journal-of-thoracic-oncology/
U2 - 10.1097/JTO.0000000000000628
DO - 10.1097/JTO.0000000000000628
M3 - Article
SN - 1556-0864
VL - 10
SP - 1341
EP - 1348
JO - Journal of Thoracic Oncology
JF - Journal of Thoracic Oncology
ER -
