Relationship between tumor size and survival in non-small cell lung cancer (NSCLC): an analysis of the Surveillance, Epidemiology, and End Results (SEER) registry
Menée à partir des données 1998-2003 des registres américains des cancers portant sur 52 287 patients atteints d'un cancer du poumon non à petites cellules de stade précoce ou présentant un envahissement local ou ganglionnaire, cette étude analyse la relation entre la taille de la tumeur et la survie des patients
INTRODUCTION : Tumor size is a known prognostic factor for early stage non-small cell lung cancer (NSCLC), but its significance in node-positive and locally invasive NSCLC has not been extensively characterized. We queried the Surveillance, Epidemiology and End Results (SEER) database to evaluate the prognostic value of tumor size for early stage as well as node-positive and locally invasive NSCLC.
METHODS : Patients in SEER registry with NSCLC diagnosed between 1998 and 2003 were analyzed. Tumor size was analyzed as a continuous variable. Other demographic variables included age, gender, race, histology, primary tumor extension, node status and primary treatment modality (surgery vs radiation). The Kaplan-Meier method was used to estimate overall survival (OS). Cox proportional hazard model was used to evaluate whether tumor size was an independent prognostic factor.
RESULTS : 52,287 eligible patients were subgrouped based on tumor extension and node status. Tumor size had a significant effect on OS in all subgroups defined by tumor extension or node status. In addition, tumor size also had statistically significant effect on OS in 15 of 16 subgroups defined by tumor extension and nodal status after adjustment for other clinical variables. Our model incorporating tumor size had significantly better predictive accuracy than our alternative model without tumor size.
CONCLUSIONS : Tumor size is an independent prognostic factor, for early stage as well as node positive and locally invasive disease. Prediction tools, such as nomograms, incorporating more detailed information not captured in detail by the routine TNM classification, may improve prediction accuracy of OS in NSCLC.
Journal of Thoracic Oncology , résumé, 2014