Description

ABSTRACT BACKGROUND AND PURPOSE: Sinonasal inverted papilloma (IP) can harbor squamous cell carcinoma (SCC). Consequently, differentiating these tumors is important. The objective of this study was to determine if MRI-based

ABSTRACT BACKGROUND AND PURPOSE: Sinonasal inverted papilloma (IP) can harbor squamous cell carcinoma (SCC). Consequently, differentiating these tumors is important. The objective of this study was to determine if MRI-based texture analysis can differentiate SCC from IP and provide supplementary information to the radiologist. MATERIALS AND METHODS: Adult patients who had IP or SCC resected were eligible (coexistent IP and SCC were excluded). Inclusion required tumor size greater than 1.5 cm and a pre-operative MRI with axial T1, axial T2, and axial T1 post-contrast sequences. Five well- established texture analysis algorithms were applied to an ROI from the largest tumor cross- section. For a training dataset, machine-learning algorithms were used to identify the most accurate model, and performance was also evaluated in a validation dataset. Based on three separate blinded reviews of the ROI, isolated tumor, and entire images, two neuroradiologists predicted tumor type in consensus. RESULTS: The IP and SCC cohorts were matched for age and gender, while SCC tumor volume was larger (p=0.001). The best classification model achieved similar accuracies for training (17 SCC, 16 IP) and validation (7 SCC, 6 IP) datasets of 90.9% and 84.6% respectively (p=0.537). The machine-learning accuracy for the entire cohort (89.1%) was better than that of the neuroradiologist ROI review (56.5%, p=0.0004) but not significantly different from the neuroradiologist review of the tumors (73.9%, p=0.060) or entire images (87.0%, p=0.748). CONCLUSION: MRI-based texture analysis has potential to differentiate SCC from IP and may provide incremental information to the neuroradiologist, particularly for small or heterogeneous tumors.

Included in this item (6)



Details

Contributors
Agent
Date Created
  • 2016-12

Machine-readable links