Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, China.
GE Healthcare, Shanghai, China.
Dentomaxillofac Radiol. 2023 Jan;52(2):20220009. doi: 10.1259/dmfr.20220009. Epub 2023 Jan 3.
OBJECTIVE: To evaluate the diagnostic performance of computed tomography (CT) radiomics analysis for differentiating pleomorphic adenoma (PA), Warthin tumor (WT), and basal cell adenoma (BCA). METHODS: A total of 189 patients with PA ( = 112), WT ( = 53) and BCA ( = 24) were divided into a training set ( = 133) and a test set ( = 56). The radiomics features were extracted from plain CT and contrast-enhanced CT images. After dimensionality reduction, plain CT, multiphase-enhanced CT, integrated radiomics signature models and radiomics score (Rad-score) were established and calculated. The receiver operating characteristic (ROC) curve analysis was taken for the assessment of the model performance, and then comparison was conducted among these models. Decision curve analysis (DCA) was adopted to assess the clinical benefits of the models. Diagnostic performances including sensitivity, specificity, and accuracy of the radiologists were evaluated. RESULTS: Seven, nine, fourteen, and fourteen optimal features were used to constructed plain scan, arterial phase, venous phase, and integrated radiomics signature models, respectively. ROC analysis showed these four models were able to differentiate PA from BCA and WT, with the area under the ROC curve (AUC) values of 0.79, 0.90, 0.87, and 0.94 in the training set, and 0.79, 0.89, 0.86, and 0.94 in the test set, respectively. The integrated model had better diagnostic performance than single-phase radiomics model, but it had similar diagnostic performance to that of the radiomics model based on the arterial phase ( > 0.05). The sensitivity, specificity, and accuracy in the diagnosis of PA were 0.86, 0.46, and 0.70 for the non-subspecialized radiologist and 0.88, 0.77, and 0.84 for the subspecialized radiologist, respectively. Six venous phase parameters were finally selected in differentiating WT from BCA. The predictive effect of the model was favorable, with AUC value of 0.95, sensitivity of 0.96, specificity of 0.83, and accuracy of 0.92. The sensitivity, specificity, and accuracy in the diagnosis between WT and BCA were 0.26, 0.87, and 0.45 for the non-subspecialized radiologist and 0.85, 0.58, and 0.77 for the subspecialized radiologist, respectively. CONCLUSION: The CT-based radiomics analysis showed favorable predictive performance for differentiating PA, WT, and BCA, thus may be helpful in the clinical decision-making.
目的：评估 CT 放射组学分析在鉴别多形性腺瘤（PA）、Warthin 瘤（WT）和基底细胞腺瘤（BCA）中的诊断性能。
方法：共纳入 189 例 PA（n=112）、WT（n=53）和 BCA（n=24）患者，将其分为训练集（n=133）和测试集（n=56）。从平扫 CT 和增强 CT 图像中提取放射组学特征。经过降维处理后，建立平扫 CT、多期增强 CT、综合放射组学特征模型和放射组学评分（Rad-score）并进行计算。采用受试者工作特征（ROC）曲线分析评估模型性能，然后对这些模型进行比较。采用决策曲线分析（DCA）评估模型的临床获益。评估放射科医师的诊断性能，包括敏感性、特异性和准确性。
结果：分别构建了基于平扫、动脉期、静脉期和综合放射组学特征模型的 7、9、14 和 14 个最优特征。ROC 分析表明，这些 4 个模型能够区分 PA 与 BCA 和 WT，在训练集中的 AUC 值分别为 0.79、0.90、0.87 和 0.94，在测试集中的 AUC 值分别为 0.79、0.89、0.86 和 0.94。综合模型的诊断性能优于单期放射组学模型，但与基于动脉期的放射组学模型的诊断性能相似（>0.05）。非专科放射科医师诊断 PA 的敏感性、特异性和准确性分别为 0.86、0.46 和 0.70，专科放射科医师分别为 0.88、0.77 和 0.84。最终选择了 6 个静脉期参数来区分 WT 和 BCA。该模型的预测效果良好，AUC 值为 0.95，敏感性为 0.96，特异性为 0.83，准确性为 0.92。非专科放射科医师诊断 WT 与 BCA 的敏感性、特异性和准确性分别为 0.26、0.87 和 0.45，专科放射科医师分别为 0.85、0.58 和 0.77。
结论：基于 CT 的放射组学分析对鉴别 PA、WT 和 BCA 具有良好的预测性能，因此可能有助于临床决策。
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