NYMC Faculty Publications

A Nomogram for the Prediction of Cerebrovascular Disease among Patients With Brain Necrosis after Radiotherapy for Nasopharyngeal Carcinoma

DOI

10.1016/j.radonc.2018.11.008

Journal Title

Radiotherapy and Oncology

First Page

34

Last Page

41

Document Type

Article

Publication Date

March 2019

Department

Medicine

Abstract

BACKGROUND AND PURPOSE: This study sought to develop and validate a nomogram to predict cerebrovascular disease (CVD) among patients with brain necrosis after radiotherapy for nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS: A total of 346 eligible patients with brain necrosis after radiotherapy for NPC were divided into a training set (n=231) and a validation set (n=115). A multivariate Cox proportional hazards regression model was used to select the significant variables for CVD prediction in the training set. Then, a nomogram was developed based on the regression model. The performance of the nomogram was assessed with respect to discrimination and calibration. All patients were classified into high- or low-risk groups based on the risk scores derived from the nomogram. Moreover, a decision curve analysis was performed with the combined training and validation sets to evaluate the clinical usefulness of the nomogram. RESULTS: Four significant predictors were identified: hypertension, statin treatment, serum level of high-density lipoprotein, and interval between radiotherapy and brain necrosis. The nomogram incorporating these four predictors showed favorable calibration and discrimination regarding the training set, with a C-index of 0.763 (95% CI, 0.694 to 0.832), which was confirmed using the validation set (C-index 0.768; 95% CI, 0.675 to 0.861). Furthermore, the nomogram successfully stratified patients into high- and low-risk groups. The decision curve indicated that our nomogram was clinically useful. CONCLUSION: The nomogram showed favorable predictive accuracy for CVD among patients with brain necrosis after radiotherapy for NPC and might aid in clinical decision making.

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