Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23–334 benign and 8–56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin’s tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis is emphasized.
影像组学分析在磁共振成像中具有表征唾液腺肿瘤的潜力。目前影像组学分析流程存在多样性，且尚未形成一致的性能报告。本综述旨在评估利用影像组学分析在MRI中表征唾液腺肿瘤的研究方法及性能表现。我们系统回顾了截至2023年7月发表的相关研究，共筛选出98项研究中的14项符合纳入标准。各项研究的样本量涵盖23-334例良性肿瘤及8-56例恶性肿瘤。最小绝对收缩与选择算子成为最常用的特征选择方法（见于8项研究）。11项研究通过交叉验证或自助法验证了所选特征的稳定性。研究共采用9种分类器构建模型，在区分良恶性唾液腺肿瘤时曲线下面积达0.74-1.00，在多形性腺瘤与沃辛瘤鉴别诊断中达0.80-0.96。分别有4项、6项和2项研究通过交叉验证、内部数据集和外部数据集验证了模型性能。各研究最终模型未出现持续一致的特征选择结果。目前MRI影像组学分析在唾液腺肿瘤表征中缺乏标准化流程，且存在多种模型构建方案，因此建立标准化的影像组学分析流程具有迫切必要性。
Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review
肿瘤(癌症)患者之家