Naganawa Shotaro, Kim John, Yip Stephen S F, Ota Yoshiaki, Srinivasan Ashok, Moritani Toshio
Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., UH B2A209K, Ann Arbor, MI, 48109, USA.
Department of Medical Physics, University of Wisconsin - Madison, 1111 Highland Ave, Rm 1005, Madison, WI, 53705-2275, USA.
Neuroradiology. 2021 Apr;63(4):547-554. doi: 10.1007/s00234-020-02607-5. Epub 2020 Nov 19.
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.
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.
Only T2-based SDLGLE was statistically significant (p = 0.048), and AUROC was 0.71. Diagnostic accuracy was 70.8%.
2D texture parameter of T2-based SDLGLE predicts SDH mutation in head and neck paragangliomas. This noninvasive technique can potentially facilitate further genetic workup.
纹理分析可量化复杂的成像特征。我们假设，通过T2加权和对比增强T1加权MRI计算得到的二维纹理能够预测头颈部副神经节瘤中的琥珀酸脱氢酶（SDH）突变状态。
我们的回顾性研究纳入了21例患者（1至4个肿瘤/患者），其头颈部有24个经病理证实的副神经节瘤。14个病灶（58%）为SDH突变阳性。所有患者均接受了T2加权和对比增强T1加权MRI序列检查。计算了依赖非均匀性归一化（DNN）、小依赖高灰度级强调（SDHGLE）和小依赖低灰度级强调（SDLGLE）这三个二维纹理特征。使用曼-惠特尼U检验比较了SDH突变体和非突变体之间的计算纹理。采用受试者操作特征（AUROC）曲线下面积来量化每个纹理的预测能力。
仅基于T2的SDLGLE具有统计学意义（p = 0.048），AUROC为0.71。诊断准确率为70.8%。
基于T2的SDLGLE二维纹理参数可预测头颈部副神经节瘤中的SDH突变。这种非侵入性技术可能有助于进一步的基因检查。
