Novel model for cancer-specific survival of patients with pulmonary sarcomatoid carcinoma: A population-based analysis and external validation.

BACKGROUND/OBJECTIVE: We aimed to develop a comprehensive and effective nomogram for predicting cancer-specific survival (CSS) in patients with pulmonary sarcomatoid carcinoma (PSC). METHODS: Data for patients diagnosed with PSC between 2004 and 2018 from the Surveillance, Epidemiology, and End Results database were retrospectively collected and randomly divided into training and internal validation sets. We then retrospectively recruited patients diagnosed with PSC to construct an external validation cohort from the Southwest Hospital. A prognostic nomogram for CSS was established using independent prognostic factors that were screened from the multivariate Cox regression analysis. The performance of the nomogram was evaluated using area under the receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), calibration diagrams, and decision curve analysis (DCA). The clinical value of the nomogram and tumor, nodes, and metastases (TNM) staging system was compared using the C-index and net reclassification index (NRI). RESULTS: Overall, 1356 patients with PSC were enrolled, including 876, 377, and 103 in the training, internal validation, and external validation sets, respectively. The C-index and ROC curves, calibration, and DCA demonstrated satisfactory nomogram performance for CSS in patients with PSC. In addition, the C-index and NRI of the nomogram suggested a significantly higher nomogram value than that of the TNM staging system. Subsequently, a web-based predictor was developed to help clinicians obtain this model easily. CONCLUSIONS: The prognostic nomogram developed in this study can conveniently and precisely estimate the prognosis of patients with PSC and individualize treatment, thereby assisting clinicians in their shared decision-making with patients.

Current status, trends, and predictions in the burden of silicosis in 204 countries and territories from 1990 to 2019.

Background: Silicosis, a severe lung disease caused by inhaling silica dust, predominantly affects workers in industries such as mining and construction, leading to a significant global public health challenge. The purpose of this study is to analyze the current disease burden of silicosis and to predict the development trend of silicosis in the future the world by extracting data from the GBD database. Methods: We extracted and analyzed silicosis prevalence, incidence, mortality, and disability-adjusted life years (DALYs) data from the Global Burden of Disease 2019 program for 204 countries and territories from 1990 to 2019. The association between the Sociodemographic Index (SDI) and the burden of age-standardized rates (ASRs) of DALYs has been examined at the regional level. Jointpoint regression analysis has been also performed to evaluate global burden trends of silicosis from 1990 to 2019. Furthermore, Nordpred age-period-cohort analysis has also been projected to predict future the burden of silicosis from 2019 to 2044. Results: In 2019, global ASRs for silicosis prevalence, incidence, mortality, and DALYs were 5.383, 1.650, 0.161, and 7.872%, respectively which are lower than that in 1990. The populations of 45-59 age group were more susceptible to silicosis, while those aged 80 or above suffered from higher mortality and DALY risks. In 2019, the most impacted nations by the burden of silicosis included China, the Democratic People's Republic of Korea, and Chile. From 1990 to 2019, most regions observed a declining burden of silicosis. An "M" shaped association between SDI and ASRs of DALYs for silicosis was observed from 1990 to 2019. The age-period-cohort analysis forecasted a decreasing trend of the burden of silicosis from 2019 to 2044. Conclusion: Despite the overall decline in the global silicosis burden from 1990 to 2019, some regions witnessed a notable burden of this disease, emphasizing the importance of targeted interventions. Our results may provide a reference for the subsequent development of appropriate management strategies.