Predictive Value of Magnetic Resonance Imaging Multi-parametric Analysis for Malignant Transformation of Sinonasal Inverted Papilloma: A Comprehensive Prediction Model

Abstract

Objective: Accurate preoperative prediction of sinonasal inverted papilloma (SNIP) malignant transformation is essential and challenging. In this study, 3.0T magnetic resonance was used for qualitative, quantitative, and multi-parametric analysis to evaluate the predictive value of magnetic resonance imaging (MRI) in malignant transformation. Methods: The data of patients with SNIP (n=83) or SNIP-transformed squamous cell carcinoma (SNIP-SCC) (n=21) were analysed retrospectively. Univariate analysis and multivariate logistic regression were used to establish models to predict the risk factors for the malignant transformation of SNIP. Receiver operating characteristic (ROC) curves were used to evaluate the ability of independent risk factors and related combination models to predict malignant transformation of SNIP. Result: Convoluted cerebriform pattern (CCP) mutation, apparent diffusion coefficient ratio (ADCr), and wash-in index (WII) 2 and 3 were independent risk factors for predicting malignant transformation of SNIP, with area under the ROC curve (AUC) values of 0.845, 0.862, 0.727, and 0.704, respectively. The AUC of the quantitative parameter model combined with ADCr and WII 2 and 3 was 0.910 for diagnosing malignant transformation. The AUC of the comprehensive model comprising all independent risk factors was 0.937, with a sensitivity, specificity, and accuracy of 90.48%, 90.36%, and 92.31%, respectively. Conclusion: Compared with assessing independent risk factors of CCP mutation, ADCr and WII, and the quantitative parameter model, the comprehensive model could improve the differential diagnosis ability of SNIP and SNIP-SCC, which provides an important imaging basis for the possible accurate preoperative evaluation of malignant transformation of SNIP.