DOI: 10.4103/jcrt.jcrt_2587_22 ISSN:

A SEER data-based nomogram for the prognostic analysis of survival of patients with Kaposi’s sarcoma

Wanghai Li, Ling Wang, Yan Zhang, Yulong Liu, Yinsheng Lin, Chengzhi Li
  • Radiology, Nuclear Medicine and imaging
  • Oncology
  • General Medicine



This study developed the first comprehensive nomogram for predicting the cancer-specific survival (CSS) of patients with Kaposi’s sarcoma (KS).


Data on the demographic and clinical characteristics of 4143 patients with KS were collected from the Surveillance, Epidemiology, and End Results (SEER) database and used for the prognostic analysis. The patients were randomly divided into two groups: training cohort (n = 2900) and validation cohort (n = 1243). Multivariate Cox regression analysis was used to identify the predictive variables for developing the first nomogram for the survival prediction of patients with KS. The new survival nomogram was further evaluated using the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plotting, and decision curve analysis (DCA).


A nomogram was developed for determining the 3-, 5-, 8-, and 10-year CSS probabilities for patients with KS. The nomogram showed that tumor stage had the greatest influence on the CSS of patients with KS, followed by demographic variables (race, marital status, and age at diagnosis) and other clinical characteristics (surgery status, chemotherapy status, tumor risk classification, and radiotherapy status). The nomogram exhibited excellent performance based on the values of the C-index, AUC, NRI, and IDI as well as calibration plots. DCA further confirmed that the nomogram had good net benefits for 3-, 5-, 8-, and 10-year survival analyses.


In this study, by using data from the SEER database, we developed the first comprehensive nomogram for analyzing the survival of patients with KS. This nomogram could serve as a convenient and reliable tool for clinicians to predict CSS probabilities for individual patients with KS.

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