Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seodaemun-gu, Seoul, Republic of Korea.
Department of Oral Pathology, Oral Cancer Research Institute, Yonsei University College of Dentistry, Seodaemun-gu, Seoul, Republic of Korea.
Oral Surg Oral Med Oral Pathol Oral Radiol. 2023 Aug;136(2):231-239. doi: 10.1016/j.oooo.2023.04.005. Epub 2023 Apr 8.
The aim of this study was to measure the ability of radiomics analysis to diagnose different stages of sialadenitis, compare the diagnostic accuracy of computed tomography (CT) and ultrasonography (US), and suggest radiomics features selected through 3 machine learning algorithms that would be helpful in discriminating between stages of sialadenitis with both imaging systems.
Wistar rats were treated to induce acute and chronic sialadenitis in the left and right submandibular glands, respectively. Contrast-enhanced CT and US of the glands were performed, followed by extirpation and histopathologic confirmation. Radiomics feature values of the glands were obtained from all images. Based on 3 feature selection methods, an optimal feature set was defined after a comparison of the receiver operating characteristic area under the curve (AUC) of each combination of 3 deep learning algorithms and 3 classification models.
The attribute features for the CT model were 2 gray-level run length matrices and 2 gray-level zone length matrices. In the US model, there were 2 gray-level co-occurrence matrices and 2 gray-level zone length matrices. The most accurate diagnostic models of CT and US yielded outstanding (AUC = 1.000) and excellent (AUC = 0.879) discrimination, respectively.
The radiomics diagnostic model using gray-level zone length matrices-based features conferred clinically outstanding discriminating ability among stages of sialadenitis using CT and excellent discrimination with US in almost all combinations of machine learning feature selections and classification models.
本研究旨在测量影像组学分析诊断涎腺炎不同阶段的能力，比较计算机断层扫描（CT）和超声检查（US）的诊断准确性，并提出通过 3 种机器学习算法选择的影像组学特征，这些特征有助于区分两种成像系统的涎腺炎阶段。
Wistar 大鼠分别接受治疗以诱导左、右颌下腺的急性和慢性涎腺炎。对腺体进行增强 CT 和 US 检查，然后进行切除和组织病理学确认。从所有图像中获得腺体的影像组学特征值。基于 3 种特征选择方法，通过比较 3 种深度学习算法和 3 种分类模型的每个组合的接收者操作特征曲线（AUC），定义最佳特征集。
CT 模型的属性特征为 2 个灰度游程长度矩阵和 2 个灰度区域长度矩阵。在 US 模型中，有 2 个灰度共生矩阵和 2 个灰度区域长度矩阵。CT 和 US 最准确的诊断模型分别产生了出色（AUC=1.000）和优秀（AUC=0.879）的区分能力。
使用灰度区域长度矩阵为特征的影像组学诊断模型使用 CT 对涎腺炎阶段进行了具有临床意义的出色区分能力，而在几乎所有机器学习特征选择和分类模型的组合中，使用 US 进行了出色的区分能力。
