AUTHOR=Zhou Yue , Gao Yinjie , Ma Xiaosen , Li Tianyi , Cui Yunying , Wang Yu , Li Ming , Zhang Dingding , Tong Anli 

TITLE=Development and internal validation of a novel predictive model for SDHB mutations in pheochromocytomas and retroperitoneal paragangliomas

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1285631

DOI=10.3389/fendo.2023.1285631

ISSN=1664-2392

ABSTRACT=Aim: To develop and validate internally a novel predictive model for SDHB mutations in pheochromocytomas and retroperitoneal paragangliomas (PPGLs).
Methods: Clinical data of patients with PPGLs presented to Peking Union Medical College Hospital from 2013 to 2022 and undergoing genetic testing was collected retrospectively. Variables were screened by the backward stepwise and clinical significance and were used to construct multivariable logistic models in 50 newly generated datasets after the multiple imputation. The bootstrapping procedure was used for internal validation. A corresponding nomogram was generated based on the model. Sensitivity analyses were also performed.
Results: 556 patients with PPGLs of which 99 had a germline SDHB-mutation were included. The prediction model revealed that younger age of onset [Odds ratio (OR): 0.93, 95% CI: 0.91-0.95], synchronous metastasis (OR: 6.43, 95% CI: 2.62-15.80), multiple lesion (OR: 0.22, 95% CI: 0.09-0.54), retroperitoneal origins (OR: 5.72, 95% CI: 3.13-10.47), negative 131I-meta-iodobenzylguanidine (MIBG) (OR: 0.34, 95% CI: 0.15-0.73), positive Octreotide scintigraphy (OR: 3.24, 95% CI: 1.25-8.43), elevated 24h urinary dopamine (DA) (OR: 1.72, 95% CI: 0.93-3.17), secretory type of NE (OR: 2.83, 95% CI: 1.22- 6.59), normal secretory function (OR: 3.04, 95% CI: 1.04-8.85) and larger tumor size (OR: 1.09, 95% CI: 0.99-1.20) were predictors of SDHB mutations in PPGLs, and showed a good and stable predictive performance with a mean area under the ROC curve (AUC) of 0.865 and coefficient of variation of 2.2%.
Conclusions: This study provided a novel and useful tool for predicting SDHB mutations through integrating easily obtained clinical data. It could help clinicians select suitable genetic testing methods, and make appropriate clinical decisions for these high-risk patients.