Abstract
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.
Original language | English |
---|---|
Article number | 343 |
Number of pages | 33 |
Journal | Biomolecules |
Volume | 13 |
Issue number | 2 |
Early online date | 9 Feb 2023 |
DOIs | |
Publication status | Published - Feb 2023 |
Keywords
- aortitis
- convolutional neural network
- machine learning
- positron emission tomography/computed tomography
- radiomics