Objective:To build and validate a radiomics and clinical nomogram for preoperative discrimination between low- and high-grade sinonasal squamous cell carcinoma (SNSCC).Methods:From January 2017 to May 2021, 167 SNSCC patients including 78 low-grade (grade Ⅰ or Ⅱ) and 89 high-grade (grade Ⅲ) were retrospectively analyzed at the Eye & ENT Hospital of Fudan University. All patients were randomly divided into a training cohort ( n=117, 64 high-grade and 53 low-grade SNSCC) and a validation cohort ( n=50, 25 high-grade and 25 low-grade SNSCC) in a ratio of 7∶3 using a stratified sampling method. The radiomics features were extracted in contrast enhanced T 1WI with manual segmentation of lesions. The least absolute shrinkage and selection operator regression was used to reduce the dimension of the radiomics features and then the radiomics model was built to predict SNSCC histological grade in training cohort. Independent clinical predicting factors were screened using logistic regression and the clinical model was built. The clinical-radiomics model was built by the radiomics features and clinical factors in the training cohort based on logistic regression and the nomogram was drawn. The receiver operator characteristic curves were drawn to evaluate the performance of clinical model, radiomics model and nomogram. The calibration curve was used to evaluate the consistency between the nomogram prediction and the actual observation risk, and the decision curve analysis (DCA) was used to evaluate the clinical applicability of the nomogram. Results:Using logistic regression analysis, the clinical model was built by the tumor primary site (OR value 7.376, 95%CI 2.517-21.618, P<0.001) and TNM stage (OR value 10.020, 95%CI 3.654-27.472, P<0.001) and the area under the curve (AUC) in the training cohort and validation cohort were 0.798 and 0.784, sensitivity were 84.4% and 84.0%, specificity were 58.5% and 68.0%, respectively. Based on the contrast enhanced T 1WI, a total of 9 radiomics features were screened for establishing the radiomics model. The AUC of radiomics model were 0.833 (sensitivity 82.8%, specificity 73.6%) and 0.851 (sensitivity 92.0%, specificity 68.0%) in the training and validation cohorts. The nomogram based on the clinical-radiomics model predicted histological grade with the highest AUC in the training cohort (AUC 0.920, sensitivity 89.1%, specificity 83.0%) and validation cohort (AUC 0.912, sensitivity 92.0%, specificity 84.0%). The calibration curve of the nomogram was close to the ideal line in both training and validation cohorts. DCA showed that the use of nomogram with a threshold in the range of <85% in training cohort, in the range of 20%-65%, 72%-90% in validation cohort, had a greater clinical application value in predicting the SNSCC histological grade. Nomogram model had a better clinical net benefit than the clinical and radiomics models. Conclusion:Nomogram combining clinical factors (tumor primary site and TNM stage) with radiomics features obtained from contrast enhanced T 1WI has a better ability for predicting histological grade of SNSCC than clinical and radiomics models.