title: MRI-Based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma.
abstract: OBJECTIVE
  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.
  
  
  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.
authors.affiliation: From the School of Computing, Informatics, and Decision Systems Engineering (S.Ramkumar, S.N., T.W., J.L.).
authors.forename: S
authors.identifier: http://orcid.org/0000-0002-8735-8472
authors.initials: S
authors.lastname: Ramkumar
authors.affiliation: Department of Biomedical Informatics (S.Ranjbar), Arizona State University, Tempe, Arizona.
authors.forename: S
authors.identifier: http://orcid.org/0000-0002-4344-1282
authors.initials: S
authors.lastname: Ranjbar
authors.affiliation: From the School of Computing, Informatics, and Decision Systems Engineering (S.Ramkumar, S.N., T.W., J.L.).
authors.forename: S
authors.identifier: http://orcid.org/0000-0002-0398-6456
authors.initials: S
authors.lastname: Ning
authors.affiliation: Departments of Otolaryngology (D.L.).
authors.forename: D
authors.identifier: http://orcid.org/0000-0002-7593-2385
authors.initials: D
authors.lastname: Lal
authors.affiliation: Radiology (C.M.Z., J.M.H.), Mayo Clinic, Phoenix, Arizona.
authors.forename: C M
authors.identifier: http://orcid.org/0000-0001-5044-2331
authors.initials: CM
authors.lastname: Zwart
authors.affiliation: Department of Radiology (C.P.W.), Mayo Clinic, Rochester, Minnesota.
authors.forename: C P
authors.identifier: http://orcid.org/0000-0001-5580-9622
authors.initials: CP
authors.lastname: Wood
authors.affiliation: Department of Radiology (S.M.W.), Mayo Clinic, Jacksonville, Florida.
authors.forename: S M
authors.identifier: http://orcid.org/0000-0001-9316-9814
authors.initials: SM
authors.lastname: Weindling
authors.affiliation: From the School of Computing, Informatics, and Decision Systems Engineering (S.Ramkumar, S.N., T.W., J.L.).
authors.forename: T
authors.identifier: http://orcid.org/0000-0002-0529-7048
authors.initials: T
authors.lastname: Wu
authors.affiliation: Department of Research (J.R.M.), Mayo Clinic, Scottsdale, Arizona.
authors.forename: J R
authors.identifier: http://orcid.org/0000-0002-2340-4708
authors.initials: JR
authors.lastname: Mitchell
authors.affiliation: From the School of Computing, Informatics, and Decision Systems Engineering (S.Ramkumar, S.N., T.W., J.L.).
authors.forename: J
authors.identifier: http://orcid.org/0000-0001-7028-3681
authors.initials: J
authors.lastname: Li
authors.affiliation: Radiology (C.M.Z., J.M.H.), Mayo Clinic, Phoenix, Arizona hoxworth.joseph@mayo.edu.
authors.forename: J M
authors.identifier: http://orcid.org/0000-0002-7179-5846
authors.initials: JM
authors.lastname: Hoxworth
mesh_terms: D000328:Adult; D000368:Aged; D002294:Carcinoma, Squamous Cell / Q000000981:diagnostic imaging* / Q000473:pathology; D003937:Diagnosis, Differential; D005260:Female; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009669:Nose Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D018308:Papilloma, Inverted / Q000000981:diagnostic imaging* / Q000473:pathology; D010255:Paranasal Sinus Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D012189:Retrospective Studies; D000077195:Squamous Cell Carcinoma of Head and Neck