Abstract
BACKGROUND: To develop a clinical prediction model and web-based survival rate calculator to predict the overall survival (OS) and cancer-specific survival (CSS) of sarcomatoid renal cell carcinoma (SRCC) for clinical diagnosis and treatment. METHODS: SRCC patient data were retrieved from Surveillance, Epidemiology, and End Results (SEER) database. Factors independently associated with survival were identified by a Cox regression analysis. Nomograms of the prediction model were constructed using a SEER training cohort and validated with a SEER validation cohort. At the same time, the decision analysis curve, receiver operating characteristic curve, and calibration curve were also used to examine and evaluate the model. A web-based survival rate calculator was constructed to help assist in the assessment of the disease condition and clinical prognosis. RESULTS: The records of 2,742 SRCC cases were retrieved from SEER, while 1,921 cases with a median OS of 14 and CSS of 32 months were used as the training cohort. The developed nomograms were more accurate than that of the American Joint Committee on Cancer staging (C-indexes of 0.767 versus 0.725 for OS and 0.775 versus 0.715 for CSS), with better discrimination than that of the American Joint Committee on Cancer (AJCC) stage model and the calibration was validated in the SEER validation cohort. The model's 3- and 5-year OS and CSS were superior to AJCC and T staging on the analysis decision curve. The prognosis prediction of SRCC established by the prediction model could be evaluated through the web-based survival rate calculator, which plays a guiding role in clinical treatment. CONCLUSIONS: Nomograms and a web-based survival rate calculator predicting the OS and CSS of SRCC patients with better discrimination and calibration were developed.