Shotaro Naganawa1, John Kim2, Stephen S F Yip3,4, Yoshiaki Ota2, Ashok Srinivasan2, Toshio Moritani2. 1. Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., UH B2A209K, Ann Arbor, MI, 48109, USA. naganawa-tky@umin.ac.jp. 2. Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., UH B2A209K, Ann Arbor, MI, 48109, USA. 3. Department of Medical Physics, University of Wisconsin - Madison, 1111 Highland Ave, Rm 1005, Madison, WI, 53705-2275, USA. 4. AIQ Solutions, Inc, 1111 Deming Way, Madison, WI, 53717, USA.
Abstract
PURPOSE: Texture analysis can quantify sophisticated imaging characteristics. We hypothesized that 2D textures computed with T2-weighted and post-contrast T1-weighted MRI can predict succinate dehydrogenase (SDH) mutation status in head and neck paragangliomas. METHODS: Our retrospective study included 21 patients (1 to 4 tumors/patient) with 24 pathologically proven paragangliomas in the head and neck. Fourteen lesions (58%) were SDH mutation-positive. All patients underwent T2-weighted and post-contrast T1-weighted MRI sequences. Three 2D texture features of dependence non-uniformity normalized (DNN), small dependence high gray level emphasis (SDHGLE), and small dependence low gray level emphasis (SDLGLE) were calculated. Computed textures between SDH mutants and non-mutants were compared using Mann-Whitney U test. Area under the receiver operating characteristic (AUROC) curve was used to quantify the predictive power of each texture. RESULTS: Only T2-based SDLGLE was statistically significant (p = 0.048), and AUROC was 0.71. Diagnostic accuracy was 70.8%. CONCLUSION: 2D texture parameter of T2-based SDLGLE predicts SDH mutation in head and neck paragangliomas. This noninvasive technique can potentially facilitate further genetic workup.
PURPOSE: Texture analysis can quantify sophisticated imaging characteristics. We hypothesized that 2D textures computed with T2-weighted and post-contrast T1-weighted MRI can predict succinate dehydrogenase (SDH) mutation status in head and neck paragangliomas. METHODS: Our retrospective study included 21 patients (1 to 4 tumors/patient) with 24 pathologically proven paragangliomas in the head and neck. Fourteen lesions (58%) were SDH mutation-positive. All patients underwent T2-weighted and post-contrast T1-weighted MRI sequences. Three 2D texture features of dependence non-uniformity normalized (DNN), small dependence high gray level emphasis (SDHGLE), and small dependence low gray level emphasis (SDLGLE) were calculated. Computed textures between SDH mutants and non-mutants were compared using Mann-Whitney U test. Area under the receiver operating characteristic (AUROC) curve was used to quantify the predictive power of each texture. RESULTS: Only T2-based SDLGLE was statistically significant (p = 0.048), and AUROC was 0.71. Diagnostic accuracy was 70.8%. CONCLUSION: 2D texture parameter of T2-based SDLGLE predicts SDH mutation in head and neck paragangliomas. This noninvasive technique can potentially facilitate further genetic workup.
Authors: Konstantinos Papaspyrou; Torsten Mewes; Heidi Rossmann; Christian Fottner; Brigitte Schneider-Raetzke; Oliver Bartsch; Mathias Schreckenberger; Karl J Lackner; Ronald G Amedee; Wolf J Mann Journal: Head Neck Date: 2011-06-20 Impact factor: 3.147
Authors: Daniel Junker; Michael Quentin; Udo Nagele; Michael Edlinger; Jonathan Richenberg; Georg Schaefer; Michael Ladurner; Werner Jaschke; Wolfgang Horninger; Friedrich Aigner Journal: World J Urol Date: 2014-08-01 Impact factor: 4.226
Authors: Brian C Jung; Julio Arevalo-Perez; John K Lyo; Andrei I Holodny; Sasan Karimi; Robert J Young; Kyung K Peck Journal: J Neuroimaging Date: 2015-08-03 Impact factor: 2.486
Authors: Samuel Joseph Withey; Stephen Perrio; Dimitra Christodoulou; Louise Izatt; Paul Carroll; Anand Velusamy; Rupert Obholzer; Valerie Lewington; Audrey Eleanor Therese Jacques Journal: Radiographics Date: 2019 Sep-Oct Impact factor: 5.333
Authors: Joost J M van Griethuysen; Andriy Fedorov; Chintan Parmar; Ahmed Hosny; Nicole Aucoin; Vivek Narayan; Regina G H Beets-Tan; Jean-Christophe Fillion-Robin; Steve Pieper; Hugo J W L Aerts Journal: Cancer Res Date: 2017-11-01 Impact factor: 12.701
Authors: Ruben T H M Larue; Gilles Defraene; Dirk De Ruysscher; Philippe Lambin; Wouter van Elmpt Journal: Br J Radiol Date: 2016-12-12 Impact factor: 3.039