Yanqi He1, Feng Zhao2, Qingbing Han3, Yiwu Zhou4,5, Shuang Zhao6. 1. Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China. heyq2004@126.com. 2. Department of Cancer Center, Sichuan Academy of Medical Sciences&Sichuan Provincial People's Hospital, Chengdu, China. 3. Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China. 4. Department of Emergency Medicine, Emergency Medical Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China. 5. Disaster Medical Center, Sichuan University, Chengdu, Sichuan, China. 6. Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China. zs_19881103@163.com.
Abstract
BACKGROUND: Lung carcinoid is a rare malignant tumor with poor survival. The current study established a nomogram model for predicting cancer-specific survival (CSS) in patients with lung carcinoid tumors. METHODS: A total of 1956 patients diagnosed with primary lung carcinoid tumors were extracted from the Surveillance, Epidemiology, and End Results database. The specific predictors of CSS for lung carcinoid tumors were identified and integrated to build a nomogram. Validation of the nomogram was conducted using parameters concordance index (C-index), calibration plots, decision curve analyses (DCAs), and the receiver operating characteristic (ROC) curve. RESULTS: Age at diagnosis, grade, histological type, N stage, M stage, surgery of the primary site, radiation of the primary site, and tumor size were independent prognostic factors of CSS. High discriminative accuracy of the nomogram model was shown in the training cohort (C-index = 0.873), which was also testified in the internal validation cohort (C-index = 0.861). In both cohorts, the calibration plots showed good concordance between the predicted and observed CSS at 3, 5, and 10 years. The DCA showed great potential for clinical application. The ROC curve showed superior survival predictive ability of the nomogram model (area under the curve = 0.868). CONCLUSIONS: We developed a practical nomogram that provided independent predictions of CSS for patients with lung carcinoid tumors. This nomogram may have the potential to assist clinicians in prognostic evaluations or developing individualized therapies for patients with this neoplasm.
BACKGROUND:Lung carcinoid is a rare malignant tumor with poor survival. The current study established a nomogram model for predicting cancer-specific survival (CSS) in patients with lung carcinoid tumors. METHODS: A total of 1956 patients diagnosed with primary lung carcinoid tumors were extracted from the Surveillance, Epidemiology, and End Results database. The specific predictors of CSS for lung carcinoid tumors were identified and integrated to build a nomogram. Validation of the nomogram was conducted using parameters concordance index (C-index), calibration plots, decision curve analyses (DCAs), and the receiver operating characteristic (ROC) curve. RESULTS: Age at diagnosis, grade, histological type, N stage, M stage, surgery of the primary site, radiation of the primary site, and tumor size were independent prognostic factors of CSS. High discriminative accuracy of the nomogram model was shown in the training cohort (C-index = 0.873), which was also testified in the internal validation cohort (C-index = 0.861). In both cohorts, the calibration plots showed good concordance between the predicted and observed CSS at 3, 5, and 10 years. The DCA showed great potential for clinical application. The ROC curve showed superior survival predictive ability of the nomogram model (area under the curve = 0.868). CONCLUSIONS: We developed a practical nomogram that provided independent predictions of CSS for patients with lung carcinoid tumors. This nomogram may have the potential to assist clinicians in prognostic evaluations or developing individualized therapies for patients with this neoplasm.
Authors: Ji Yoon Yoon; Keith Sigel; Jacob Martin; Robyn Jordan; Mary Beth Beasley; Cardinale Smith; Andrew Kaufman; Juan Wisnivesky; Michelle Kang Kim Journal: J Thorac Oncol Date: 2018-11-08 Impact factor: 15.609
Authors: Katelyn A Young; Enobong Efiong; James T Dove; Joseph A Blansfield; Marie A Hunsinger; Jeffrey L Wild; Mohsen M Shabahang; Matthew A Facktor Journal: Ann Surg Oncol Date: 2017-02-06 Impact factor: 5.344