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Quantitative assessment of normal submandibular glands and submandibular sialadenitis using CT texture analysis: A retrospective study

2020; Elsevier BV; Volume: 132; Issue: 1 Linguagem: Inglês

10.1016/j.oooo.2020.10.007

2212-4411

Kotaro Ito, Hirotaka Muraoka, Naohisa Hirahara, Eri Sawada, Shunya Okada, Takashi Kaneda,

Head and Neck Cancer Studies

Objective 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. Study Design 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. Results 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). Conclusions CT texture analysis was able to quantitatively distinguish between normal and diseased submandibular glands. It therefore may have the potential to detect SS. 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.