CT影像组学标签预测胸腺瘤风险分类和临床分期
Radiomics Signatures of Computed Tomography Imaging for Predicting Risk Categorization and Clinical Stage of Thymomas.
点评：尽管本研究得到的诊断效能并不高，详细研究设计、方法和样本量支持结论，对胸腺瘤的临床诊疗具有一定临床价值。但本研究未纳入胸腺癌病例导致早晚期数据差别很大，多平台数据是否会对结果产生影响。作者也提出了TNM分期与CT纹理特征的关系有待进一步研究。
PURPOSE:
The aim of this study is to develop and compare performance of radiomics signatures using texture features extracted from noncontrast enhanced CT (NECT) and contrast enhanced CT (CECT) images for preoperative predicting risk categorization and clinical stage of thymomas.
本研究的目的是利用平扫CT（NECT）和增强CT（CECT）图像提取的纹理特征建立用于术前预测胸腺瘤的风险分类和临床分期的影像组学标签，并比较其预测效能。
MATERIALS AND METHODS:
Between January 2010 and October 2018, 199 patients with surgical resection and histopathologically confirmed thymoma were enrolled in this retrospective study. We extracted 841 radiomics features separately from volume of interest (VOI) in NECT and CECT images.
这项回顾性研究入选了2010年1月至2018年10月期间199例手术切除和组织病理学证实的胸腺瘤患者。我们在NECT和CECT图像中分别从感兴趣体（VOI）中提取了841个放射组学特征。
The features with poor reproducibility and highly redundancy were removed. Then a least absolute shrinkage and selection operator method (LASSO) logistic regression model with 10-fold cross validation was used for further feature selection and radiomics signatures build.
首先剔除了重复性差和高度冗余的特征，然后使用具有10倍交叉验证的最小绝对值收敛和选择算子方法（LASSO）逻辑回归模型进行进一步的特征选择和构建影像组学标签。
The predictive performances of radiomics signatures were assessed by receiver operating characteristic (ROC) analysis. The areas under the receiver operating characteristic curve (AUC) between radiomics signatures were compared by using Delong test.
通过受试者操作特征（ROC）分析评估影像组学标签的预测性能。应用Delong检验比较组学标签间的ROC曲线下面积（AUC）的差异。
RESULT:
In differentiating high risk thymomas from low risk thymomas, the AUC, sensitivity, and specificity were 0.801(95% CI 0.740-0.863), 0.752 and 0.767 for radiomics signature based on NECT images, and 0.827 (95% CI 0.771 -0.884), 0.798, and 0.722 for radiomics signature based on CECT images. But there was no significant difference (p=0.365) between them.
在区分高危胸腺瘤和低危胸腺瘤时，基于NECT和CECT图像的组学标签的AUC、灵敏度和特异性分别为0.801（95％CI 0.740-0.863）、0.752和0.767，以及0.827（95％CI 0.771 -0.884）、0.798和0.722。但两者间没有显著统计学差异（p = 0.365）。
In differentiating advanced stage thymomas from early stage thymomas, the AUC, sensitivity, and specificity were 0.829 (95%CI 0.757-0.900), 0.712, and 0.806 for radiomics signature based on NECT images and 0.860 (95%CI 0.803-0.917), 0.699, and 0.889 for radiomics signature based on CECT images. There was no significant difference (p=0.069) between them.
在区分晚期胸腺瘤的早期胸腺瘤中，基于NECT和CECT图像的组学标签的AUC、敏感性和特异性分别为0.829（95％CI 0.757-0.900），0.712和0.806，以及0.860（95％CI 0.803-0.917）、0.699和0.889。两者间也没有显著统计学差异（p = 0.069）。
The accuracy was 0.819 for radiomics signature based on NECT images, 0.869 for radiomics signature based on CECT images, and 0.779 for radiologists. Both radiomics signatures had a better performance than radiologists. But there was significant difference (p = 0.025) only between CECT radiomics signature and radiologists.
基于NECT图像的影像组学标签的准确度为0.819，基于CECT图像的影像组学标签的准确度为0.869，放射科医师的准确度为0.779。与放射医师比较，两种影像组学标签均获得了更好的诊断效能。但仅在CECT组学标签和放射医师间存在显著差异（p = 0.025）。
CONCLUSION:
Radiomics signatures based on texture analysis from NECT and CECT images could be utilized as noninvasive biomarkers for differentiating high risk thymomas from low risk thymomas and advanced stage thymomas from early stage thymoma. As a quantitative method, radiomics signature can provide complementary diagnostic information and help to plan personalized treatment for patients with thymomas.
基于NECT和CECT图像纹理分析的影像组学标签可用于鉴别高危胸腺瘤与低危胸腺瘤以及早晚期胸腺瘤的无创性生物学标记。作为一种定量方法，影像组学标签能够提供一些补充诊断信息，有助于为胸腺瘤患者制定个性化治疗方案。
图1：患者选择流程图
Figure 3: Te top 10 features contributed to radiomics signatures based on NECT images (a) and CECT images (b) weighted by standardized regression coefcients according to LASSO logistic regression model to diferentiate high risk thymomas from low risk thymomas.
Figure 4: Attributing weights of radiomics features based on NECT images (a) and CECT images (b) selected by LASSO model to diferentiate advanced thymomas from early thymomas.
Figure 6: ROC curves analysis of radiomics signatures based on CECT images and NECT images for diferentiating high risk thymomas from low risk thymomas.
Figure 7: ROC curves analysis of radiomics signatures for diferentiating advanced thymoma from early stage thymomas.
Wang X, Sun W, Liang H, Mao X, Lu Z. Radiomics Signatures of Computed Tomography Imaging for Predicting Risk Categorization and Clinical Stage of Thymomas. Biomed Res Int. 2019 May 28;2019:3616852. doi: 10.1155/2019/3616852.eCollection 2019. PubMed PMID: 31275968
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