Prognostic Nomograms for Predicting Overall Survival and Cancer‐Specific Survival of Patients With Malignant Pheochromocytoma and Paraganglioma

Zheng, Lei; Gu, Yalong; Silang, Jiangcun; Wang, Jinlong; Luo, Feng; Zhang, Baopeng; Li, Chuan‐Hong; Wang, Feng

doi:10.3389/fendo.2021.684668

Cited by 5 publications

(1 citation statement)

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“…A clinical nomogram is a valid and reliable prediction model that combines multiple prognostic factors to accurately calculate and predict individual survival ( 25 ). Nomograms have been used extensively in many previous studies, such as those on oropharyngeal cancer ( 26 ), young breast cancer ( 27 ), low-grade endometrial stromal sarcoma ( 28 ), and both malignant pheochromocytoma and paraganglioma ( 29 ). In terms of liver cancer, a nomogram has been constructed to predict OS in PHNET cases by Zhang et al ( 13 ); however, this report did not take into account the effect of competing events on PHNET-specific death, which lowered the confidence of the results.…”

Section: Discussionmentioning

confidence: 99%

Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor

Lin

Ding

et al. 2022

Front. Med.

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Primary hepatic neuroendocrine tumor (PHNET) is rare liver cancer and related prognostic factors are unclear. The aim of this study was to analyze the prognostic risk factors of patients with PHNETs and establish an assessment model for prognosis. The clinical information of 539 patients with PHNETs who met the criteria for inclusion was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly assigned to the training (269 cases) and validation sets (270 cases). Prognostic factors in patients with PHNETs were screened using the Cox proportional regression model and Fine–Gray competing risk model. Based on the training set analysis using the Fine–Gray competing risk model, a nomogram was constructed to predict cumulative probabilities for PHNET-specific death. The performance of the nomogram was measured by using receiver operating characteristic curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA). No differences in clinical baseline characteristics between the training and validation sets were observed, and the Fine–Gray analysis showed that surgery and more than one primary malignancy were associated with a low cumulative probability of PHNET-specific death. The training set nomograms were well-calibrated and had good discriminative ability, and good agreement between predicted and observed survival was observed. Patients with PHNETs with a high-risk score had a significantly increased risk of PHNET-specific death and non-PHNET death. Surgical treatment and the number of primary malignancies were found to be independent protective factors for PHNETs. The competing risk nomogram has high accuracy in predicting disease-specific survival (DSS) for patients with PHNETs, which may help clinicians to develop individualized treatment strategies.

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