The nomograms for predicting overall and cancer-specific survival in elderly patients with early-stage lung cancer: A population-based study using SEER database - Yu, Gen; Liu, Xiaozhu; Li, Yunhe; Zhang, Yang; Yan, Ruxin; Zhu, Lingfeng; Wang, Zhongjian (Frontiers in Public Health, 2022)
Purpose: Lung cancer is the leading cause of death from cancer and the number of operable elderly lung cancer patients is increasing, with advanced age being associated with a poorer prognosis. However, there is no easy and comprehensive prognostic assessment method for these patients. Methods: Clinicopathological data from the SEER database were downloaded for patients aged 65 years or older with TNM stage I-II lung cancer from 2004-2018. Patients from 2004 to 2015 were randomized into a training group (n = 16457) and a validation group (n = 7048). Data from 2016-2018 (n = 6232) were used for external validation. Univariate and multivariate Cox proportional hazards regression analyses were used to identify independent prognostic factors associated with overall survival (OS) and cancer-specific survival (CSS) in the training set, and two nomogram prognostic models were constructed. In turn, overall survival (OS) and cancer-specific survival (CSS) are predicted for patients at 1, 3 and 5 years. The validity, accuracy, discrimination, predictive ability, and clinical utility of the models were assessed based on the concordance index (C-index), calibration curves, area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC). Decision curve analysis (DCA) was used to assess the clinical value of the models. Results: A total of 29737 patients were included. Univariate and multivariate analyses suggested that age, race, gender, marriage, disease grade, AJCC stage, T-stage, surgery, radiotherapy, chemotherapy, and tumor size were independent risk factors for patient prognosis. These 11 variables were included in nomogram to predict OS and CSS of patients. C-indexes for the training, validation and external validation sets were 0.730 (95% CI, 0.709-0.751), 0.734 (95% CI, 0.722-0.746) and 0.750 (95% CI, 0.734-0.766), respectively. Tools such as Calibration curve demonstrated the good performance of the created nomograms. Conclusion: This study identified risk factors for survival in operable elderly lung cancer patients and established a new column line graph for predicting OS and CSS in these patients. The model has good clinical application and can be a good clinical decision-making support tool for physicians and patients.