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基于动态对比增强 MRI 的定量纹理分析在原发性胸腺瘤与胸腺癌鉴别诊断中的应用。

Quantitative texture analysis based on dynamic contrast enhanced MRI for differential diagnosis between primary thymic lymphoma from thymic carcinoma.

机构信息

Department of Radiology, Jiangsu Province People's Hospital, Nanjing Medical University First Affiliated Hospital, No. 300 Guangzhou Road, Nanjing, 210029, Jiangsu, People's Republic of China.

出版信息

Sci Rep. 2022 Jul 24;12(1):12629. doi: 10.1038/s41598-022-16393-y.

Abstract

To evaluate the value of texture analysis based on dynamic contrast enhanced MRI (DCE-MRI) in the differential diagnosis of thymic carcinoma and thymic lymphoma. Sixty-nine patients with pathologically confirmed (thymic carcinoma, n = 32; thymic lymphoma, n = 37) were enrolled in this retrospective study. K, K and V maps were automatically generated, and texture features were extracted, including mean, median, 5th/95th percentile, skewness, kurtosis, diff-variance, diff-entropy, contrast and entropy. The differences in parameters between the two groups were compared and the diagnostic efficacy was calculated. The K-related significant features yielded an area under the curve (AUC) of 0.769 (sensitivity 90.6%, specificity 51.4%) for the differentiation between thymic carcinoma and thymic lymphoma. The K-related significant features yielded an AUC of 0.780 (sensitivity 87.5%, specificity 62.2%). The V-related significant features yielded an AUC of 0.807 (sensitivity 75.0%, specificity 78.4%). The combination of DCE-MRI textural features yielded an AUC of 0.962 (sensitivity 93.8%, specificity 89.2%). Five parameters were screened out, including age, K-entropy, K-entropy, V-entropy, and V-P95. The combination of these five parameters yielded the best discrimination efficiency (AUC of 0.943, 93.7% sensitivity, 81.1% specificity). Texture analysis of DCE-MRI may be helpful to distinguish thymic carcinoma from thymic lymphoma.

摘要

目的

评估基于动态对比增强磁共振成像(DCE-MRI)的纹理分析在胸腺癌和胸腺瘤鉴别诊断中的价值。

方法

回顾性分析经病理证实的 69 例胸腺癌(n=32)和胸腺瘤(n=37)患者的 DCE-MRI 资料。自动生成 K、K 和 V 图,并提取纹理特征,包括均值、中位数、5%/95%分位数、偏度、峰度、差异方差、差异熵、对比度和熵。比较两组间参数差异,并计算诊断效能。

结果

K 相关的显著特征区分胸腺癌和胸腺瘤的曲线下面积(AUC)为 0.769(敏感度 90.6%,特异度 51.4%)。K 相关的显著特征区分胸腺癌和胸腺瘤的 AUC 为 0.780(敏感度 87.5%,特异度 62.2%)。V 相关的显著特征区分胸腺癌和胸腺瘤的 AUC 为 0.807(敏感度 75.0%,特异度 78.4%)。DCE-MRI 纹理特征联合的 AUC 为 0.962(敏感度 93.8%,特异度 89.2%)。筛选出 5 个参数,包括年龄、K 熵、K 熵、V 熵和 V-P95。这 5 个参数的组合具有最佳的鉴别效率(AUC 为 0.943,敏感度 93.7%,特异度 81.1%)。

结论

DCE-MRI 纹理分析有助于鉴别胸腺癌和胸腺瘤。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37de/9309158/c38ca828928f/41598_2022_16393_Fig1_HTML.jpg

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