Mao Kaijing, Wong Lun M, Zhang Rongli, So Tiffany Y, Shan Zhiyi, Hung Kuo Feng, Ai Qi Yong H
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.
Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.
Cancers (Basel). 2023 Oct 10;15(20):4918. doi: 10.3390/cancers15204918.
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.
放射组学分析有可能在磁共振成像（MRI）上对唾液腺肿瘤（SGTs）进行特征描述。放射组学分析的程序多种多样，且未报告一致的性能。本综述评估了使用放射组学分析在MRI上对SGTs进行特征描述的研究方法和性能。我们系统回顾了截至2023年7月发表的使用放射组学分析在MRI上对SGTs进行特征描述的研究。总共98项研究中有14项符合条件。每项研究检查了23 - 334例良性和8 - 56例恶性SGTs。最小绝对收缩和选择算子（LASSO）是最常用的特征选择方法（八项研究中使用）。十一项研究使用交叉验证或自助法证实了所选特征的稳定性。使用九种分类器构建模型，用于区分良性和恶性SGTs的曲线下面积（AUCs）为0.74至1.00，用于区分多形性腺瘤和沃辛瘤的AUCs为0.80至0.96。分别在四项、六项和两项研究中使用交叉验证、内部和外部数据集对性能进行了验证。在各项研究的最终模型中，没有单一特征始终出现。在MRI上对SGTs进行特征描述时，放射组学分析没有使用标准化程序，并且提出了各种模型。强调了对放射组学分析标准化程序的需求。
