目的: 探讨基于增强CT纹理分析在鉴别诊断胸腺瘤和高密度胸腺囊肿方面的价值。方法: 回顾性分析经手术病理证实的共123例胸腺肿物患者的完整资料,其中胸腺瘤88例,高密度胸腺囊肿35例。采取完全随机方法将所有患者按7∶3的比例分为训练组(n=87)和验证组(n=36)。使用3D Slicer软件,由2名放射科医师分别在平扫、动脉期及静脉期图像逐层沿靶病灶边缘手动勾画感兴趣区(region of interest, ROI),并提取纹理特征;首先通过组内相关系数(intraclass correlation coeffcient, ICC)评估2名医师提取的纹理特征的一致性;应用最小绝对值收敛和选择算子(least absolute shrinkage and selection operator, LASSO)算法对纹理特征进行筛选和降维;构建Logistic回归模型;依据特征权重系数加权生成影像组学评分(Rad-score);采用受试者工作特征(receiver operating characteristic, ROC)曲线分析模型的鉴别性能,计算曲线下面积(area under curve, AUC);最终通过决策曲线分析(decision curve analysis, DCA)从临床获益角度验证模型的实用价值。结果: 2名医师提取的纹理特征的一致性良好(ICC＞0.75)。临床模型在训练组和验证组中鉴别诊断胸腺瘤和高密度胸腺囊肿的AUC分别为0.87和0.90。基于平扫、动脉期、静脉期、平扫＋动脉期、平扫＋静脉期、动脉＋静脉期及三期联合的影像组学模型在训练组中鉴别诊断胸腺瘤和高密度胸腺囊肿的AUC分别为0.90、0.91、0.95、0.94、0.98、0.95和0.93,在验证组中分别为0.94、0.95、0.97、0.94、0.96、0.97和0.96。DCA分析结果表明,平扫＋静脉期模型的临床受益较好。结论: 基于增强CT纹理分析中平扫＋静脉期联合模型对鉴别诊断胸腺瘤和高密度胸腺囊肿有相对较高的应用价值。
Objective: To investigate the diagnostic value of contrast-enhanced CT texture analysis in differentiating thymoma and high-density thymic cysts. Methods: The complete data of 123 patients with thymic tumors confirmed by surgery and pathology were retrospectively analyzed, including 88 patients with thymoma and 35 patients with high-density thymic cysts. All patients were randomly divided into training group (n=87) and validation group (n=36) according to the ratio of 7∶3. Using 3D Slicer, two radiologists manually delineated the region of interest (ROI) along the edge of the target lesion in plain, arterial, and venous images, and extracted the texture features. Intraclass correlation coefficient (ICC) was used to evaluate the consistency of texture features extracted by 2 physicians. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature dimensionality reduction and selection, followed by logistic regression modeling. A radiomics score (Rad-score) was calculated by weighting selected features based on their coefficients. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic performance, and the area under curve (AUC) was calculated. Finally, decision curve analysis (DCA) was employed to validate clinical net benefits across threshold probabilities. Results: The agreement of the texture features extracted by 2 physicians was good (ICC＞0.75). The AUC for the differential diagnosis of thymoma and high-density thymic cysts in the training set and validation set was 0.87 and 0.90. The AUC of plain, arterial, venous, plain＋arterial, plain＋venous, arterial＋venous, and three-phases were 0.90, 0.91, 0.95, 0.94, 0.98, 0.95 and 0.93 in the training set, while in the validation set, the AUC were 0.94, 0.95, 0.97, 0.94, 0.96, 0.97 and 0.96. The results of the DCA analysis showed that the plain＋venous phase model had a good clinical benefit. Conclusion: Based on the combined model of plain＋venous stage in contrast-enhanced CT texture analysis has relatively high value in the differential diagnosis of thymoma and high-density thymic cyst.
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