A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images: Initial analysis

Lisa Duff*, Andrew F. Scarsbrook, Sarah L. Mackie, Russell Frood, Marc Bailey, Ann W. Morgan, Charalampos Tsoumpas

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
17 Downloads (Pure)

Abstract

Background The aim of this study was to explore the feasibility of assisted diagnosis of active (peri-)aortitis using radiomic imaging biomarkers derived from [F-18]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. Methods The aorta was manually segmented on FDG PET-CT in 50 patients with aortitis and 25 controls. Radiomic features (RF) (n = 107), including SUV (Standardized Uptake Value) metrics, were extracted from the segmented data and harmonized using the ComBat technique. Individual RFs and groups of RFs (i.e., signatures) were used as input in Machine Learning classifiers. The diagnostic utility of these classifiers was evaluated with area under the receiver operating characteristic curve (AUC) and accuracy using the clinical diagnosis as the ground truth. Results Several RFs had high accuracy, 84% to 86%, and AUC scores 0.83 to 0.97 when used individually. Radiomic signatures performed similarly, AUC 0.80 to 1.00. Conclusion A methodological framework for a radiomic-based approach to support diagnosis of aortitis was outlined. Selected RFs, individually or in combination, showed similar performance to the current standard of qualitative assessment in terms of AUC for identifying active aortitis. This framework could support development of a clinical decision-making tool for a more objective and standardized assessment of aortitis.

Original languageEnglish
Pages (from-to)3315–3331
Number of pages17
JournalJournal of Nuclear Cardiology
Volume29
Early online date23-Mar-2022
DOIs
Publication statusPublished - Dec-2022

Keywords

  • Large-vessel vasculitis
  • FDG PET
  • CT
  • Radiomic feature analysis
  • Diagnosis
  • Giant cell arteritis
  • LARGE-VESSEL VASCULITIS
  • GIANT-CELL ARTERITIS
  • POLYMYALGIA-RHEUMATICA
  • F-18-FDG PET
  • EANM
  • COHORT

Cite this