常规和放射组学特征预测前纵隔肿块术前评估的病理。
Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses.
机构信息
Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Medical Imaging Department. Hospital Clinic of Barcelona, 170 Villarroel street, Barcelona 08036, Spain.
Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
出版信息
Lung Cancer. 2023 Apr;178:206-212. doi: 10.1016/j.lungcan.2023.02.014. Epub 2023 Feb 21.
OBJECTIVES
The aim of this study was to differentiate benign from malignant tumors in the anterior mediastinum based on computed tomography (CT) imaging characteristics, which could be useful in preoperative planning. Additionally, our secondary aim was to differentiate thymoma from thymic carcinoma, which could guide the use of neoadjuvant therapy.
MATERIALS AND METHODS
Patients referred for thymectomy were retrospectively selected from our database. Twenty-five conventional characteristics were evaluated by visual analysis, and 101 radiomic features were extracted from each CT. In the step of model training, we applied support vector machines to train classification models. Model performance was assessed using the area under the receiver operating curves (AUC).
RESULTS
Our final study sample comprised 239 patients, 59 (24.7 %) with benign mediastinal lesions and 180 (75.3 %) with malignant thymic tumors. Among the malignant masses, there were 140 (58.6 %) thymomas, 23 (9.6 %) thymic carcinomas, and 17 (7.1 %) non-thymic lesions. For the benign versus malignant differentiation, the model that integrated both conventional and radiomic features achieved the highest diagnostic performance (AUC = 0.715), in comparison to the conventional (AUC = 0.605) and radiomic-only (AUC = 0.678) models. Similarly, regarding thymoma versus thymic carcinoma differentiation, the model that integrated both conventional and radiomic features also achieved the highest diagnostic performance (AUC = 0.810), in comparison to the conventional (AUC = 0.558) and radiomic-only (AUC = 0.774) models.
CONCLUSION
CT-based conventional and radiomic features with machine learning analysis could be useful for predicting pathologic diagnoses of anterior mediastinal masses. The diagnostic performance was moderate for differentiating benign from malignant lesions and good for differentiating thymomas from thymic carcinomas. The best diagnostic performance was achieved when both conventional and radiomic features were integrated in the machine learning algorithms.
目的
本研究旨在基于 CT 成像特征区分前纵隔良、恶性肿瘤，为术前规划提供帮助。此外，我们的次要目的是区分胸腺瘤和胸腺癌，以指导新辅助治疗的应用。
材料和方法
我们从数据库中回顾性选择了接受胸腺切除术的患者。通过视觉分析评估了 25 个常规特征，并从每个 CT 中提取了 101 个放射组学特征。在模型训练步骤中，我们应用支持向量机训练分类模型。使用接收者操作特征曲线下的面积（AUC）评估模型性能。
结果
本研究最终纳入 239 例患者，其中 59 例（24.7%）为良性纵隔病变，180 例（75.3%）为恶性胸腺肿瘤。恶性肿块中，胸腺瘤 140 例（58.6%），胸腺癌 23 例（9.6%），非胸腺病变 17 例（7.1%）。对于良性与恶性的区分，整合常规和放射组学特征的模型具有最高的诊断性能（AUC=0.715），优于仅常规（AUC=0.605）和仅放射组学（AUC=0.678）模型。同样，对于胸腺瘤与胸腺癌的区分，整合常规和放射组学特征的模型也具有最高的诊断性能（AUC=0.810），优于仅常规（AUC=0.558）和仅放射组学（AUC=0.774）模型。
结论
基于 CT 的常规和放射组学特征结合机器学习分析，有助于预测前纵隔肿块的病理诊断。对于区分良性和恶性病变，诊断性能为中等，对于区分胸腺瘤和胸腺癌，诊断性能较好。当常规和放射组学特征都整合到机器学习算法中时，可获得最佳诊断性能。
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