PDF(Pubmed)
Abstract：
UNASSIGNED: Pulmonary epithelioid hemangioendothelioma (PEH) is a rare vascular tumour, and its early diagnosis remains challenging. This study aims to comprehensively analyse the imaging features of PEH and develop a model for predicting PEH.
UNASSIGNED: Retrospective and pooled analyses of imaging findings were performed in PEH patients at our center (n=25) and in published cases (n=71), respectively. Relevant computed tomography (CT) images were extracted and used to build a deep learning model for PEH identification and differentiation from other diseases.
UNASSIGNED: In this study, bilateral multiple nodules/masses (n=19) appeared to be more common with most nodules less than 2 cm. In addition to the common types and features, the pattern of mixed type (n=4) and isolated nodules (n=4), punctate calcifications (5/25) and lymph node enlargement were also observed (10/25). The presence of pleural effusion is associated with a poor prognosis in PEH. The deep learning model, with an area under the receiver operating characteristic curve (AUC) of 0.71 [95% confidence interval (CI): 0.69-0.72], has a differentiation accuracy of 100% and 74% for the training and test sets respectively.
UNASSIGNED: This study confirmed the heterogeneity of the imaging findings in PEH and showed several previously undescribed types and features. The current deep learning model based on CT has potential for clinical application and needs to be further explored in the future.
UNASSIGNED: Retrospective and pooled analyses of imaging findings were performed in PEH patients at our center (n=25) and in published cases (n=71), respectively. Relevant computed tomography (CT) images were extracted and used to build a deep learning model for PEH identification and differentiation from other diseases.
UNASSIGNED: In this study, bilateral multiple nodules/masses (n=19) appeared to be more common with most nodules less than 2 cm. In addition to the common types and features, the pattern of mixed type (n=4) and isolated nodules (n=4), punctate calcifications (5/25) and lymph node enlargement were also observed (10/25). The presence of pleural effusion is associated with a poor prognosis in PEH. The deep learning model, with an area under the receiver operating characteristic curve (AUC) of 0.71 [95% confidence interval (CI): 0.69-0.72], has a differentiation accuracy of 100% and 74% for the training and test sets respectively.
UNASSIGNED: This study confirmed the heterogeneity of the imaging findings in PEH and showed several previously undescribed types and features. The current deep learning model based on CT has potential for clinical application and needs to be further explored in the future.
摘要：
■肺上皮样血管内皮瘤（PEH）是一种罕见的血管肿瘤，其早期诊断仍然具有挑战性。本研究旨在全面分析PEH的成像特征，并建立预测PEH的模型。
■对我们中心的PEH患者（n=25）和已发表病例（n=71）的影像学表现进行了回顾性和汇总分析。分别。提取相关计算机断层扫描（CT）图像，并将其用于构建深度学习模型，以识别PEH并与其他疾病区分。
■在这项研究中，双侧多发结节/肿块（n=19）似乎更常见，大多数结节小于2cm。除了常见的类型和功能，混合型(n=4)和孤立结节(n=4)的模式，还观察到点状钙化（5/25）和淋巴结肿大（10/25）。胸腔积液的存在与PEH的不良预后相关。深度学习模型,受试者工作特征曲线下面积（AUC）为0.71[95％置信区间（CI）：0.69-0.72]，训练集和测试集的区分精度分别为100%和74%。
■这项研究证实了PEH中成像发现的异质性，并显示了几种以前未描述的类型和特征。当前基于CT的深度学习模型具有临床应用潜力，未来需要进一步探索。
■对我们中心的PEH患者（n=25）和已发表病例（n=71）的影像学表现进行了回顾性和汇总分析。分别。提取相关计算机断层扫描（CT）图像，并将其用于构建深度学习模型，以识别PEH并与其他疾病区分。
■在这项研究中，双侧多发结节/肿块（n=19）似乎更常见，大多数结节小于2cm。除了常见的类型和功能，混合型(n=4)和孤立结节(n=4)的模式，还观察到点状钙化（5/25）和淋巴结肿大（10/25）。胸腔积液的存在与PEH的不良预后相关。深度学习模型,受试者工作特征曲线下面积（AUC）为0.71[95％置信区间（CI）：0.69-0.72]，训练集和测试集的区分精度分别为100%和74%。
■这项研究证实了PEH中成像发现的异质性，并显示了几种以前未描述的类型和特征。当前基于CT的深度学习模型具有临床应用潜力，未来需要进一步探索。
