TY - JOUR T1 - MRI-Based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma JF - American Journal of Neuroradiology JO - Am. J. Neuroradiol. DO - 10.3174/ajnr.A5106 AU - S. Ramkumar AU - S. Ranjbar AU - S. Ning AU - D. Lal AU - C.M. Zwart AU - C.P. Wood AU - S.M. Weindling AU - T. Wu AU - J.R. Mitchell AU - J. Li AU - J.M. Hoxworth Y1 - 2017/03/02 UR - http://www.ajnr.org/content/early/2017/03/02/ajnr.A5106.abstract N2 - BACKGROUND AND PURPOSE: Because sinonasal inverted papilloma can harbor squamous cell carcinoma, differentiating these tumors is relevant. The objectives of this study were to determine whether MR imaging–based texture analysis can accurately classify cases of noncoexistent squamous cell carcinoma and inverted papilloma and to compare this classification performance with neuroradiologists' review.MATERIALS AND METHODS: Adult patients who had inverted papilloma or squamous cell carcinoma resected were eligible (coexistent inverted papilloma and squamous cell carcinoma were excluded). Inclusion required tumor size of >1.5 cm and preoperative MR imaging with axial T1, axial T2, and axial T1 postcontrast 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. On the basis of 3 separate blinded reviews of the ROI, isolated tumor, and entire images, 2 neuroradiologists predicted tumor type in consensus.RESULTS: The inverted papilloma (n = 24) and squamous cell carcinoma (n = 22) cohorts were matched for age and sex, while squamous cell carcinoma tumor volume was larger (P = .001). The best classification model achieved similar accuracies for training (17 squamous cell carcinomas, 16 inverted papillomas) and validation (7 squamous cell carcinomas, 6 inverted papillomas) datasets of 90.9% and 84.6%, respectively (P = .537). For the combined training and validation cohorts, the machine-learning accuracy (89.1%) was better than that of the neuroradiologists' ROI review (56.5%, P = .0004) but not significantly different from the neuroradiologists' review of the tumors (73.9%, P = .060) or entire images (87.0%, P = .748).CONCLUSIONS: MR imaging–based texture analysis has the potential to differentiate squamous cell carcinoma from inverted papilloma and may, in the future, provide incremental information to the neuroradiologist.AbbreviationsDOSTDiscrete Orthonormal Stockwell TransformGFBGabor Filter BanksGLCMGray-Level Co-occurrence MatrixIPinverted papillomaLBPlocal binary patternsLoGHistLaplacian of Gaussian histogramPCprincipal componentSCCsquamous cell carcinomaT1axial T1-weighted MRI pulse sequenceT2axial T2-weighted MRI pulse sequence with frequency-selective fat suppressionT1Caxial T1-weighted postcontrast MRI pulse sequence with frequency-selective fat suppression ER -