Xu Zhong1, Lei Ye1,2, TingWei Su1, Jing Xie3, Weiwei Zhou1, Yiran Jiang1, Lei Jiang1, Guang Ning1,2, Weiqing Wang1,2. 1. Shanghai Key Laboratory for Endocrine Tumors, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of Chinese Health Ministry, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 2. Laboratory for Endocrine & Metabolic Diseases of Institute of Health Science, Shanghai Jiao Tong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China. 3. Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Abstract
OBJECTIVE: No single histological or molecular marker is diagnostic for malignant phaeochromocytomas and paragangliomas (PPGLs). This study aimed to establish and evaluate a prognostic nomogram to improve the prediction of metastatic probability in individual PPGL patients. METHODS: Three hundred and 47 consecutive PPGL patients from January 2002 through December 2014 were randomly divided into a training set (n=208) and a validation set (n=139). A multivariate logistic regression analysis of selected prognostic features was performed, and a nomogram to predict metastasis was constructed. Discrimination and calibration were employed to evaluate the performance of the nomogram. Clinical usefulness was calculated using decision curve analysis. RESULTS: The overall metastatic rate was 10.6%. Primary tumour size, primary tumour location, vascular invasion, ERBB-2 overexpression, SDHB mutation and catecholamine type were associated with malignancy in the logistic analysis and were included in the nomogram. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.872 (95% confidence interval [CI], 0.819-0.914) in the training set. The validation set showed good discrimination, with an AUC of 0.870 (95% CI, 0.803-0.921). The nomogram was well calibrated, with no significant difference between the predicted and the observed probabilities (Hosmer-Lemeshow test: P=.510 for the training set; .314 for the validation set). Decision curve analysis revealed that molecular markers (ERBB-2 overexpression and SDHB mutation) could increase the clinical benefit of the nomogram. CONCLUSION: Our results support the use of the present biomarker-based nomogram, which has good discriminative ability, to predict the metastatic probability of PPGLs.
OBJECTIVE: No single histological or molecular marker is diagnostic for malignant phaeochromocytomas and paragangliomas (PPGLs). This study aimed to establish and evaluate a prognostic nomogram to improve the prediction of metastatic probability in individual PPGL patients. METHODS: Three hundred and 47 consecutive PPGL patients from January 2002 through December 2014 were randomly divided into a training set (n=208) and a validation set (n=139). A multivariate logistic regression analysis of selected prognostic features was performed, and a nomogram to predict metastasis was constructed. Discrimination and calibration were employed to evaluate the performance of the nomogram. Clinical usefulness was calculated using decision curve analysis. RESULTS: The overall metastatic rate was 10.6%. Primary tumour size, primary tumour location, vascular invasion, ERBB-2 overexpression, SDHB mutation and catecholamine type were associated with malignancy in the logistic analysis and were included in the nomogram. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.872 (95% confidence interval [CI], 0.819-0.914) in the training set. The validation set showed good discrimination, with an AUC of 0.870 (95% CI, 0.803-0.921). The nomogram was well calibrated, with no significant difference between the predicted and the observed probabilities (Hosmer-Lemeshow test: P=.510 for the training set; .314 for the validation set). Decision curve analysis revealed that molecular markers (ERBB-2 overexpression and SDHB mutation) could increase the clinical benefit of the nomogram. CONCLUSION: Our results support the use of the present biomarker-based nomogram, which has good discriminative ability, to predict the metastatic probability of PPGLs.