Multiparametric MRI-based radiomics nomogram for predicting malignant transformation of sinonasal inverted papilloma

AIM: To investigate the feasibility of a radiomics nomogram model for predicting malignant transformation in sinonasal inverted papilloma (IP) based on radiomic signature and clinical risk factors. MATERIALS AND METHODS: This single institutional retrospective review included a total of 143 patients with IP and 75 patients with IP with malignant transformation to squamous cell carcinoma (IP-SCC). All patients underwent surgical pathology and had preoperative magnetic resonance imaging (MRI) and computed tomography (CT) sinus studies between June 2014 and February 2022. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1WI), T2-weighted images (T2WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) were performed to select the features extracted from the sequences mentioned above. Independent clinical risk factors were identified by multivariate logistic regression analysis. Radiomics nomogram was constructed by incorporating independent clinical risk factors and radiomics signature. Based on discrimination and calibration, the diagnostic performance of the nomogram was evaluated. RESULTS: Twelve radiomics features were selected to develop the radiomics model with an area under the curve (AUC) of 0.987 and 0.989, respectively. Epistaxis (p=0.011), T2 equal signal (p=0.0 03), extranasal invasion (p<0.001), and loss of convoluted cerebriform pattern (p=0.0 02) were identified as independent clinical predictors. The radiomics nomogram model showed excellent calibration and discrimination (AUC: 0.993, 95% confidence interval [CI]: 0.985-1.00 and 0.990, 95% CI: 0.974-1.00) in the training and validation sets, respectively. CONCLUSION: The nomogram that the combined radiomics signature and clinical risk factors showed a satisfactory ability to predict IP-SCC. (c) 2023 Published by Elsevier Ltd on behalf of The Royal College of Radiologists.