A Competing Risk Nomogram for Predicting Cancer-Specific Death of Patients With Maxillary Sinus Carcinoma - Hu, Mingbin; Li, Xiancai; Gu, Weiguo; Mei, Jinhong; Liu, Dewu; Chen, Shaoqing (Frontiers in Oncology, 2021)
Objectives: Herein, we purposed to establish and verify a competing risk nomogram for estimating the risk of cancer-specific death (CSD) in Maxillary Sinus Carcinoma (MSC) patients. Methods: The data of individuals with MSC used in this study was abstracted from the (SEER) Surveillance, Epidemiology, and End Results data resource as well as from the First Affiliated Hospital of Nanchang University (China). The risk predictors linked to CSD were identified using the CIF (cumulative incidence function) along with the Fine-Gray proportional hazards model on the basis of univariate analysis coupled with multivariate analysis implemented in R-software. After that, a nomogram was created and verified to estimate the three- and five-year CSD probability. Results: Overall, 478 individuals with MSC were enrolled from the SEER data resource, with a three- and five-year cumulative incidence of CSD after diagnosis of 42.1% and 44.3%, respectively. The Fine-Gray analysis illustrated that age, histological type, N stage, grade, surgery and T stage were independent predictors linked to CSD in the SEER-training data set (n=343). These variables were incorporated in the prediction nomogram. The nomogram was well calibrated and demonstrated remarkable estimation accuracy in the internal validation data set (n=135) abstracted from the SEER data resource and the external validation data set (n=200). The nomograms were well-calibrated and had good discriminative ability with concordance indexes (c-indexes) of 0.810, 0.761, and 0.755 for 3- and 5-year prognosis prediction of MSC-specific mortality in the training cohort, internal validation and external validation cohort, respectively . Conclusions: The competing risk nomogram constructed herein proved to be an optimal assistant tool for estimating CSD in individuals with MSC.