Ito Kotaro, Muraoka Hirotaka, Hirahara Naohisa, Sawada Eri, Okada Shunya, Kaneda Takashi
Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan.
Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan.
Oral Surg Oral Med Oral Pathol Oral Radiol. 2021 Jul;132(1):112-117. doi: 10.1016/j.oooo.2020.10.007. Epub 2020 Oct 27.
The purpose of this study was to quantitatively assess normal submandibular glands and submandibular sialadenitis (SS) using computed tomography (CT) texture analysis as part of radiomics quantitative analysis.
In total, 31 patients with unilateral SS who underwent head and neck magnetic resonance imaging (MRI) and CT and were retrospectively reviewed. Submandibular glands with abnormal signals (STIR: high, T2-weighted image: high, T1-weighted image: low) on MRI were identified as SS. The radiomics features of the contralateral normal submandibular glands and SS were analyzed using an open-access software, MaZda Version 3.3. Sixteen radiomics features were selected with Fisher and probability of error and average correlation coefficient methods in MaZda from 279 original parameters calculated for each of the normal and SS glands. The results were statistically analyzed with the Wilcoxon rank sum test.
One gray-level co-occurrence matrix feature and 9 gray-level run length matrix features displayed significant differences between normal submandibular glands and glands with SS (P < .05).
CT texture analysis was able to quantitatively distinguish between normal and diseased submandibular glands. It therefore may have the potential to detect SS.
本研究旨在利用计算机断层扫描（CT）纹理分析作为放射组学定量分析的一部分，对正常下颌下腺和下颌下腺涎腺炎（SS）进行定量评估。
总共回顾性分析了31例接受头颈部磁共振成像（MRI）和CT检查的单侧SS患者。MRI上信号异常（短TI反转恢复序列[STIR]：高信号，T2加权像：高信号，T1加权像：低信号）的下颌下腺被确定为SS。使用开源软件MaZda 3.3版分析对侧正常下颌下腺和SS的放射组学特征。在MaZda中，从为每个正常和SS腺体计算的279个原始参数中，采用Fisher法、错误概率法和平均相关系数法选择了16个放射组学特征。结果采用Wilcoxon秩和检验进行统计学分析。
1个灰度共生矩阵特征和9个灰度游程长度矩阵特征在正常下颌下腺和患有SS的腺体之间显示出显著差异（P < 0.05）。
CT纹理分析能够定量区分正常和患病的下颌下腺。因此，它可能具有检测SS的潜力。
