An MRI‐Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors

Abstract

Preoperative discrimination between malignant and benign sinonasal tumors is important for treatment plan selection. To build and validate a radiomic nomogram for preoperative discrimination between malignant and benign sinonasal tumors. Retrospective. In all, 197 patients with histopathologically confirmed 84 benign and 113 malignant sinonasal tumors. Fast-spin-echo (FSE) T1 -weighted and fat-suppressed FSE T2 -weighted imaging on a 1.5T and 3.0T MRI. T1 and fat-suppressed T2 -weighted images were selected for feature extraction. The least absolute shrinkage selection operator (LASSO) algorithm was applied to establish a radiomic score. Multivariate logistic regression analysis was applied to determine independent risk factors, and the radiomic score was combined to build a radiomic nomogram. The nomogram was assessed in a training dataset (n = 138/3.0T MRI) and tested in a validation dataset (n = 59/1.5T MRI). Independent t-test or Wilcoxon's test, chi-square-test, or Fisher's-test, univariate analysis, LASSO, multivariate logistic regression analysis, area under the curve (AUC), Hosmer-Lemeshow test, decision curve, and the Delong test. In the validation dataset, the radiomic nomogram could differentiate benign from malignant sinonasal tumors with an AUC of 0.91. There was no significant difference in AUC between the combined radiomic score and radiomic nomogram (P > 0.05), and the radiomic nomogram showed a relatively higher AUC than the combined radiomic score. There was a significant difference in AUC between each two of the following models (the radiomic nomogram vs. the clinical model, all P < 0.001; the combined radiomic score vs. the clinical model, P = 0.0252 and 0.0035, respectively, in the training and validation datasets). The radiomic nomogram outperformed the radiomic scores and clinical model. The radiomic nomogram combining the clinical model and radiomic score is a simple, effective, and reliable method for patient risk stratification. 4 TECHNICAL EFFICACY STAGE: 2.