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. 2020 May 2;12(5):1146. doi: 10.3390/cancers12051146

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© 2020 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Combination of CCPRS and clinical variables to improve risk assessment and stratification. (A) A nomogram was constructed to quantify risk assessment and predict CSS probability for individual patients. (B) Time-dependent ROC analysis was performed to evaluate the predictive power of the nomogram for 1-year and 3-year CSS. (C) Calibration analysis indicated the nomogram has a high accuracy of CSS prediction. (D) An integrated decision tree was generated to optimize the risk stratification for MIBC patients. (E) In the training cohort, MIBC patients in the high-risk group exhibited worst CSS compared to other groups (p < 0.0001). (F) Significant difference of CSS was observed among different risk subgroups in the validation cohort.