ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2022
A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images: Initial analysis.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Aortitis as a High-Risk Vascular Syndrome: Integrating Phenotype-Driven Diagnosis, Multidisciplinary Assessment, and Personalised Management.Journal of personalized medicine · 2026Review
- A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [Diagnostics (Basel, Switzerland) · 2025Article
- An assessment of PET and CMR radiomic features for the detection of cardiac sarcoidosis.Frontiers in nuclear medicine · 2024Article
- The clinical value of quantitative cardiovascular molecular imaging: a step towards precision medicine.The British journal of radiology · 2023Review
- Exploring the Utility of Cardiovascular Magnetic Resonance Radiomic Feature Extraction for Evaluation of Cardiac Sarcoidosis.Diagnostics (Basel, Switzerland) · 2023Article
- An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images.Biomolecules · 2023Article
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
Abstract
backgroundThe aim of this study was to explore the feasibility of assisted diagnosis of active (peri-)aortitis using radiomic imaging biomarkers derived from [
methodsThe 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.
resultsSeveral 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.
conclusionA 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.
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Registered trials
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