School of Medicine, University of Leeds, Leeds LS2 9JT, UK.
Institute of Medical and Biological Engineering, University of Leeds, Leeds LS2 9JT, UK.
Biomolecules. 2023 Feb 9;13(2):343. doi: 10.3390/biom13020343.
The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A-RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C-Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.
本研究旨在开发和验证一种自动化的流水线，利用源自[18F]-氟脱氧葡萄糖正电子发射断层扫描-计算机断层扫描（FDG PET-CT）图像的放射组学成像生物标志物来辅助活动性大动脉炎的诊断。通过卷积神经网络（CNN）对大动脉炎和对照组患者的 FDG PET-CT 进行自动分割。FDG PET-CT 数据集分为训练集（43 例大动脉炎：21 例对照组）、测试集（12 例大动脉炎：5 例对照组）和验证集（24 例大动脉炎：14 例对照组）。从分割数据中提取并协调了放射组学特征（RF），包括 SUV 指标。构建了三种放射组学指纹：A-RF 具有高诊断效用，可去除高度相关的 RF；B 采用主成分分析（PCA）；C-随机森林内在特征选择。使用准确性和接收器操作特征曲线下的面积（AUC）评估诊断效用。在训练、测试和外部验证数据集中，多个 RF 和指纹具有较高的 AUC 值（AUC>0.8），通过平衡准确性得到证实。在多个多中心数据集上实现了良好的诊断性能表明放射组学流水线具有可泛化性。这些发现可用于构建自动化临床决策工具，以便无论观察者的经验如何，都能进行客观和标准化的评估。
