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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Prognostic Factors in Non-Small Cell Lung Cancer Less Than 3 Centimeters: Actuarial Analysis, Accumulative Incidence
Juan C Peñalver Cuesta1, Carlos Jordá Aragón2, Nuria Mancheño Franch3
1Servicio de Cirugía Torácica, Fundación Instituto Valenciano de Oncología (FIVO), Valencia, España.
Introduction:
In TNM classification, factors determining the tumor (T) component in non-small cell lung cancer have scarcely changed over time and are still based solely on anatomical features. Our objective was to study the influence of these and other morphopathological factors on survival.
Methods:
A total of 263 patients undergoing lung resection due to stage I non-small cell lung cancer ≤3cm in diameter were studied. A survival analysis and competing-risk estimate study was made on the basis of clinical, surgical and pathological variables using actuarial analysis and accumulative incidence methods, respectively. A risk model was then generated from the results.
Results:
Survival at 5 and 10 years was 79.8 and 74.3%, respectively. The best prognostic factors were presence of symptoms, smoking habit and FEV1>60%, number of resected nodes>7, squamous histology, absence of vascular invasion, absence of visceral pleural invasion and presence of invasion more proximal than the lobar bronchus. All these were statistically significant according to the actuarial method. The factor "age<50 years" was close to the margin of statistical significance. Pleural invasion and vascular invasion were entered in the multivariate analysis. The competing-risk analysis showed a probability of death due to cancer of 14.3 and 35.1% at 5 and 10 years, respectively. Significant variables in the univariate and multivariate analyses were similar, with the exception of FEV1>60%.
Conclusions:
Pleural invasion and vascular invasion determine survival or risk of death due to non-small cell lung cancer ≤3cm and can be used for generating a predictive risk model.
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