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 Cindex 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. (c) 2023 Asian Surgical Association and Taiwan Robotic Surgery Association. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).