Ting Wei Su1, Xu Zhong1, Lei Ye1,2, Wei Song1, Lei Jiang1, Jing Xie3, Yiran Jiang1, Weiwei Zhou1, Cui Zhang1, Luming Wu1, Guang Ning1,2, Weiqing Wang4,5. 1. Shanghai Clinical Center for Endocrine and Metabolic Diseases, Department of Endocrinology, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, 197 Ruijin 2nd Road, 200025, Shanghai, China. 2. Shanghai Key Laboratory for Endocrine Tumors, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai, China. 3. Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 4. Shanghai Clinical Center for Endocrine and Metabolic Diseases, Department of Endocrinology, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, 197 Ruijin 2nd Road, 200025, Shanghai, China. wqingw61@163.com. 5. Shanghai Key Laboratory for Endocrine Tumors, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai, China. wqingw61@163.com.
Abstract
PURPOSE: Up to 40% of patients with pheochromocytomas or paragangliomas (PPGLs) carry a germline mutation. This study aimed to build a nomogram using clinical information to predict the probability of germline mutation in PPGLs. METHODS: The data were collected from 563 patients who were diagnosed with PPGLs between 2002 and 2015. Clinical and pathologic features were assessed with a multivariable logistic regression analysis to predict the presence of germline mutations. A nomogram to predict the probability of germline mutation was constructed with R software. Discrimination and calibration were employed to evaluate the performance of the nomogram. RESULTS: By multivariate analysis, age at manifestation, bilateral, or multifocal tumors and family history were identified as independent predictors of the presence of any germline mutation. The nomogram was then developed using these three variables. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0. 841 (95% confidence interval [CI], 0.809-0.871). The calibration plot indicated that the nomogram-predicted probabilities compared very well with the actual probabilities (Hosmer-Lemeshow test: P = 0.888). CONCLUSION: The nomogram is a valuable predictive tool for the presence of germline mutations in patients with PPGLs.
PURPOSE: Up to 40% of patients with pheochromocytomas or paragangliomas (PPGLs) carry a germline mutation. This study aimed to build a nomogram using clinical information to predict the probability of germline mutation in PPGLs. METHODS: The data were collected from 563 patients who were diagnosed with PPGLs between 2002 and 2015. Clinical and pathologic features were assessed with a multivariable logistic regression analysis to predict the presence of germline mutations. A nomogram to predict the probability of germline mutation was constructed with R software. Discrimination and calibration were employed to evaluate the performance of the nomogram. RESULTS: By multivariate analysis, age at manifestation, bilateral, or multifocal tumors and family history were identified as independent predictors of the presence of any germline mutation. The nomogram was then developed using these three variables. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0. 841 (95% confidence interval [CI], 0.809-0.871). The calibration plot indicated that the nomogram-predicted probabilities compared very well with the actual probabilities (Hosmer-Lemeshow test: P = 0.888). CONCLUSION: The nomogram is a valuable predictive tool for the presence of germline mutations in patients with PPGLs.
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Authors: P F Plouin; L Amar; O M Dekkers; M Fassnacht; A P Gimenez-Roqueplo; J W M Lenders; C Lussey-Lepoutre; O Steichen Journal: Eur J Endocrinol Date: 2016-05 Impact factor: 6.664