TY - JOUR
T1 - Mri-based texture analysis to differentiate sinonasal squamous cell carcinoma from inverted papilloma
AU - Ramkumar, S.
AU - Ranjbar, S.
AU - Ning, S.
AU - Lal, D.
AU - Zwart, C. M.
AU - Wood, C. P.
AU - Weindling, S. M.
AU - Wu, Teresa
AU - Mitchell, J. R.
AU - Li, Jing
AU - Hoxworth, J. M.
N1 - Publisher Copyright:
© Copyright 2016 Stryker.
PY - 2017/5
Y1 - 2017/5
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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85019930590&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019930590&partnerID=8YFLogxK
U2 - 10.3174/ajnr.A5106
DO - 10.3174/ajnr.A5106
M3 - Article
C2 - 28255033
AN - SCOPUS:85019930590
SN - 0195-6108
VL - 38
SP - 1019
EP - 1025
JO - American Journal of Neuroradiology
JF - American Journal of Neuroradiology
IS - 5
ER -