Background/Objectives: Differentiating between benign and malignant parotid gland tumors (PGT) is essential for establishing the treatment strategy, which is greatly influenced by the tumor’s histology. The objective of this study was to evaluate the role of MRI-based radiomics in the differentiation between Warthin’s tumors (WT) and malignant tumors (MT), two entities that proved to present overlapping imaging features on conventional and functional MRI sequences. Methods: In this retrospective study, a total of 106 PGT (66 WT, 40 MT) with confirmed histology were eligible for radiomic analysis, which were randomly split into a training group (79 PGT; 49 WT; 30 MT) and a testing group (27 PGT; 17 WT, 10 MT). The radiomic features were extracted from 3D segmentations of PGT performed on the following sequences: PROPELLER T2-weighted images and the ADC map, using a dedicated software. First- and second-order features were derived for each lesion, using original and filtered images. Results: After employing several feature reduction techniques, including LASSO regression, three final radiomic parameters were identified to be the most significant in distinguishing between the two studied groups, with fair AUC values that ranged between 0.703 and 0.767. All three radiomic features were used to construct a Radiomic Score that presented the highest diagnostic performance in distinguishing between WT and MT, achieving an AUC of 0.785 in the training set, and 0.741 in the testing set. Conclusions: MRI-based radiomic features have the potential to serve as promising novel imaging biomarkers for discriminating between Warthin’s tumors and malignant tumors in the parotid gland. Nevertheless, it is still to prove how radiomic features can consistently achieve higher diagnostic performance, and if they can outperform alternative imaging methods, ideally in larger, multicentric studies.
背景/目的：鉴别腮腺肿瘤的良恶性对制定治疗策略至关重要，而治疗方案很大程度上取决于肿瘤的组织学特征。本研究旨在评估基于磁共振成像的影像组学在区分沃辛瘤与恶性肿瘤中的作用，这两种肿瘤在常规及功能磁共振序列中已被证实存在影像学特征重叠现象。方法：本回顾性研究共纳入106例经组织学确诊的腮腺肿瘤（66例沃辛瘤，40例恶性肿瘤）进行影像组学分析，随机分为训练组（79例；49例沃辛瘤，30例恶性肿瘤）和测试组（27例；17例沃辛瘤，10例恶性肿瘤）。通过专业软件从PROPELLER T2加权图像及ADC图的腮腺肿瘤三维分割区域中提取影像组学特征，并基于原始图像与滤波图像计算每个病灶的一阶及二阶特征。结果：采用包括LASSO回归在内的多种特征降维技术后，最终确定三个影像组学参数对区分两组肿瘤最具显著性，其受试者工作特征曲线下面积值介于0.703至0.767之间。整合这三个特征构建的影像组学评分在区分沃辛瘤与恶性肿瘤中展现出最佳诊断效能，训练集曲线下面积达0.785，测试集为0.741。结论：基于磁共振的影像组学特征有望成为区分腮腺沃辛瘤与恶性肿瘤的新型影像学生物标志物。然而，影像组学特征能否持续获得更高诊断效能，以及是否优于其他影像学方法，仍需通过更大规模的多中心研究加以验证。
肿瘤(癌症)患者之家