CT放射组学标签可预测胸腺上皮肿瘤的风险分类(CT组学方法文献列表) ...

CT放射组学标签可预测胸腺上皮肿瘤的风险分类。CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors

目的：本研究旨在评估基于3D计算机断层扫描（CT）的放射组学方法的性能，临床和语义特征，以预测胸腺上皮肿瘤（TET）的病理学分类。

方法：该研究共纳入190例接受手术切除并经病理证实为TET的患者。所有患者在治疗前均接受了非增强CT（NECT）扫描和增强CT（CECT）扫描。从NECT和CECT图像中的感兴趣体积中提取了每个患者的396个手动设计（hand-crafted）的放射组学特征。我们比较了TET患者之间的三个临床特征和六个语义特征（观察到的放射组学特征）。建立了两个三分类放射组学模型（RM），两个相应的临床RM和两个相应的临床语义RM，以识别TET的类型。受试者工作特性曲线（AUC）和准确度（ACC）下的区域对于评估不同的模型很有用。

结果：190例患者中，低危胸腺瘤83例，高危胸腺瘤58例，胸腺癌49例。各组的临床特征（年龄）和语义特征（纵隔脂肪浸润，纵隔淋巴结肿大和胸腔积液）显著不同（P <0.001）。在验证集中，基于NECT的临床RM（AUC = 0.770对于低风险胸腺瘤，0.689对于高风险胸腺瘤，对于胸腺癌为0.783； ACC = 0.569）优于基于CECT的临床语义RM（低危胸腺瘤的AUC = 0.785，高危胸腺瘤的AUC = 0.576，胸腺癌为0.774； ACC = 0.483）。

结论：基于NECT的和基于CECT的RM可能提供一种无创的方法来区分低危胸腺瘤，高危胸腺瘤和胸腺癌，而基于NECT的RM表现更好。

进展知识：Radimics模型可用于TET的病理学分类的术前预测。

关键词：计算机体层摄影术；机器学习；病理学分类放射线学胸腺上皮肿瘤。

Objectives: This study aims to assess the performance of radiomics approaches based on 3D computed tomography (CT), clinical and semantic features in predicting the pathological classification of thymic epithelial tumors (TETs).

Methods: A total of 190 patients who underwent surgical resection and had pathologically confirmed TETs were enrolled in this retrospective study. All patients underwent non-contrast-enhanced CT (NECT) scans and contrast-enhanced CT (CECT) scans before treatment. A total of 396 hand-crafted radiomics features of each patient were extracted from the volume of interest in NECT and CECT images. We compared three clinical features and six semantic features (observed radiological traits) between patients with TETs. Two triple-classification radiomics models (RMs), two corresponding clinical RMs, and two corresponding clinical-semantic RMs were built to identify the types of the TETs. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were useful to evaluate the different models.

Results: Of the 190 patients, 83 had low-risk thymoma, 58 had high-risk thymoma, and 49 had thymic carcinoma. Clinical features (Age) and semantic features (mediastinal fat infiltration, mediastinal lymph node enlargement, and pleural effusion) were significantly different among the groups(P < 0.001). In the validation set, the NECT-based clinical RM (AUC = 0.770 for low-risk thymoma, 0.689 for high-risk thymoma, and 0.783 for thymic carcinoma; ACC = 0.569) performed better than the CECT-based clinical-semantic RM (AUC = 0.785 for low-risk thymoma, 0.576 for high-risk thymoma, and 0.774 for thymic carcinoma; ACC = 0.483).

Conclusions: NECT-based and CECT-based RMs may provide a non-invasive method to distinguish low-risk thymoma, high-risk thymoma, and thymic carcinoma, and NECT-based RMs performed better.

Advances in knowledge: Radiomics models may be used for the preoperative prediction of the pathological classification of TETs.

Keywords: computed tomography; machine learning; pathologic classification; radiomics; thymic epithelial tumors.

Liu J, Yin P, Wang S, Liu T, Sun C, Hong N. CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors. Front Oncol. 2021 Feb 26;11:628534. doi: 10.3389/fonc.2021.628534. PMID: 33718203; PMCID: PMC7953900.