School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, Xinjiang, China.
Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, No 137, LiYuShan South Road, Urumqi, 830011, Xinjiang, China.
Parasitol Res. 2022 Dec;121(12):3393-3401. doi: 10.1007/s00436-022-07663-9. Epub 2022 Oct 1.
The purpose of this study was to establish a clinical prediction model for the differential diagnosis of pulmonary cystic echinococcosis (CE) and pulmonary abscess according to computed tomography (CT)-based radiomics signatures and clinical indicators. This is a retrospective single-centre study. A total of 117 patients, including 53 with pulmonary CE and 64 with pulmonary abscess, were included in our study and were randomly divided into a training set (n = 95) and validation set (n = 22). Radiomics features were extracted from CT images, a radiomics signature was constructed, and clinical indicators were evaluated to establish a clinical prediction model. Finally, a model combining imaging radiomics features and clinical indicators was constructed. The performance of the nomogram, radiomics signature and clinical prediction model was evaluated and validated with the training and test datasets, and then the three models were compared. The radiomics signature of this study was established by 25 features, and the radiomics nomogram was constructed by using clinical factors and the radiomics signature. Finally, the areas under the receiver operating characteristic curve (AUCs) for the training set and test set were 0.970 and 0.983, respectively. Decision curve analysis showed that the radiologic nomogram was better than the clinical prediction model and individual radiologic characteristic model in differentiating pulmonary CE from pulmonary abscess. The radiological nomogram and models based on clinical factors and individual radiomics features can distinguish pulmonary CE from pulmonary abscess and will be of great help to clinical diagnoses in the future.
本研究旨在根据基于计算机断层扫描（CT）的放射组学特征和临床指标建立用于鉴别诊断肺包虫囊肿（CE）和肺脓肿的临床预测模型。这是一项回顾性单中心研究。共纳入 117 例患者，包括 53 例肺 CE 和 64 例肺脓肿患者，将其随机分为训练集（n=95）和验证集（n=22）。从 CT 图像中提取放射组学特征，构建放射组学特征，评估临床指标以建立临床预测模型。最后，构建了一种结合影像学放射组学特征和临床指标的模型。使用训练集和测试集评估和验证了列线图、放射组学特征和临床预测模型的性能，并对三个模型进行了比较。该研究的放射组学特征由 25 个特征建立，放射组学列线图由临床因素和放射组学特征构建。最终，训练集和测试集的受试者工作特征曲线（AUC）下面积分别为 0.970 和 0.983。决策曲线分析表明，放射列线图在鉴别肺 CE 和肺脓肿方面优于临床预测模型和个体放射学特征模型。基于临床因素和个体放射组学特征的放射列线图和模型可用于区分肺 CE 和肺脓肿，这将对未来的临床诊断有很大帮助。
