Radiomics Nomograms Based on Multi-Parametric MRI for Preoperative Differential Diagnosis of Malignant and Benign Sinonasal Tumors: A Two-Centre Study

Abstract

ObjectivesTo investigate the efficacy of multi-parametric MRI-based radiomics nomograms for preoperative distinction between benign and malignant sinonasal tumors.MethodsData of 244 patients with sinonasal tumor (training set, n=192; test set, n=52) who had undergone pre-contrast MRI, and 101 patients who underwent post-contrast MRI (training set, n=74; test set, n=27) were retrospectively analyzed. Independent predictors of malignancy were identified and their performance were evaluated. Seven radiomics signatures (RSs) using maximum relevance minimum redundancy (mRMR), and the least absolute shrinkage selection operator (LASSO) algorithm were established. The radiomics nomograms, comprising the clinical model and the RS algorithms were built: one based on pre-contrast MRI (RNWOC); the other based on pre-contrast and post-contrast MRI (RNWC). The performances of the models were evaluated with area under the curve (AUC), calibration, and decision curve analysis (DCA) respectively.ResultsThe efficacy of the clinical model (AUC=0.81) of RNWC was higher than that of the model (AUC=0.76) of RNWOC in the test set. There was no significant difference in the AUC of radiomic algorithms in the test set. The RS-T1T2 (AUC=0.74) and RS-T1T2T1C (RSWC, AUC=0.81) achieved a good distinction efficacy in the test set. The RNWC and the RNWOC showed excellent distinction (AUC=0.89 and 0.82 respectively) in the test set. The DCA of the nomograms showed better clinical usefulness than the clinical models and radiomics signatures.ConclusionsThe radiomics nomograms combining the clinical model and RS can be accurately, safely and efficiently used to distinguish between benign and malignant sinonasal tumors.

Differentiation of Sinonasal NKT From Diffuse Large B-Cell Lymphoma Using Machine Learning and MRI-Based Radiomics

“…Radiomics performs high-throughput mining and quantification of medical images, extracts, and analyses of almost infinite advanced quantitative features by identifying tumor heterogeneity and microenvironment, thus contributing to the detection, diagnosis, and assessment of cancer 17,18 . The applications of radiomics for the head and neck have demonstrated promising results in the differential diagnosis of malignant tumors 19–23 . To the best of our knowledge, no studies yet use the approach of radiomics to identify these 2 types of sinonasal NHLs.…”

“…17,18 The applications of radiomics for the head and neck have demonstrated promising results in the differential diagnosis of malignant tumors. [19][20][21][22][23] To the best of our knowledge, no studies yet use the approach of radiomics to identify these 2 types of sinonasal NHLs.…”