• Han Zhang, Hexiang Wang, Dapeng Hao, Yaqiong Ge, Guangyao Wan, Jun Zhang, Shunli Liu, Yu Zhang, and Deguang Xu.
• The Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
• J Magn Reson Imaging. 2021 Jan 1; 53 (1): 141-151.

BackgroundPreoperative discrimination between malignant and benign sinonasal tumors is important for treatment plan selection.PurposeTo build and validate a radiomic nomogram for preoperative discrimination between malignant and benign sinonasal tumors.Study TypeRetrospective.PopulationIn all, 197 patients with histopathologically confirmed 84 benign and 113 malignant sinonasal tumors.Field Strength/SequencesFast-spin-echo (FSE) T1 -weighted and fat-suppressed FSE T2 -weighted imaging on a 1.5T and 3.0T MRI.AssessmentT1 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).Statistical TestsIndependent 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.ResultsIn 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.Data ConclusionThe radiomic nomogram combining the clinical model and radiomic score is a simple, effective, and reliable method for patient risk stratification.Level Of Evidence4 TECHNICAL EFFICACY STAGE: 2.© 2020 International Society for Magnetic Resonance in Medicine.

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