Article Dans Une Revue JACC: Cardiovascular Imaging Année : 2023

18F-FDG-Based Radiomics and Machine Learning

Résumé

BACKGROUND Fluorine-18 fluorodeoxyglucose ( 18 F-FDG)-positron emission tomography (PET)/computed tomography (CT) results in better sensitivity for prosthetic valve endocarditis (PVE) diagnosis, but visual image analysis results in relatively weak specificity and significant interobserver variability.

OBJECTIVES The primary objective of this study was to evaluate the performance of a radiomics and machine learningbased analysis of 18 F-FDG PET/CT (PET-ML) as a major criterion for the European Society of Cardiology score using machine learning as a major imaging criterion (ESC-ML) in PVE diagnosis. The secondary objective was to assess performance of PET-ML as a standalone examination.

METHODS All 18 F-FDG-PET/CT scans performed for suspected aortic PVE at a single center from 2015 to 2021 were retrospectively included. The gold standard was expert consensus after at least 3 months' follow-up. The machine learning (ML) method consisted of manually segmenting each prosthetic valve, extracting 31 radiomics features from the segmented region, and training a ridge logistic regressor to predict PVE. Training and hyperparameter tuning were done with a cross-validation approach, followed by an evaluation on an independent test database.

RESULTS A total of 108 patients were included, regardless of myocardial uptake, and were divided into training (n ¼ 68) and test (n ¼ 40) cohorts. Considering the latter, PET-ML findings were positive for 13 of 22 definite PVE cases and 3 of 18 rejected PVE cases (59% sensitivity, 83% specificity), thus leading to an ESC-ML sensitivity of 72% and a specificity of 83%.

CONCLUSIONS The use of ML for analyzing 18 F-FDG-PET/CT images in PVE diagnosis was feasible and beneficial, particularly when ML was included in the ESC 2015 criteria. Despite some limitations and the need for future developments, this approach seems promising to optimize the role of 18 F-FDG PET/CT in PVE diagnosis.

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hal-05583510 , version 1 (07-04-2026)

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Thomas Godefroy, Gauthier Frécon, Antoine Asquier-Khati, Diana Mateus, Raphaël Lecomte, et al.. 18F-FDG-Based Radiomics and Machine Learning. JACC: Cardiovascular Imaging, 2023, 16 (7), pp.951 - 961. ⟨10.1016/j.jcmg.2023.01.020⟩. ⟨hal-05583510⟩
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