Neuroborreliosis affects the nervous system after systemic infection with the spirochete
Borrelia burgdorferi
. Previously, cerebral vasculitis has been regarded as an extremely rare complication of ...neuroborreliosis. The data on the long-term outcome in patients with cerebral vasculitis due to neuroborreliosis are limited. The objective of this study was to perform a longitudinal analysis of cases of neuroborreliosis-associated cerebral vasculitis. We recruited all patients (
n
= 11) diagnosed with neuroborreliosis-associated in three neurological departments in an East German region. Inclusion criteria were sudden neurological deficits, magnetic resonance (MR) imaging findings that conform to cerebral ischemia or brain infarction, intrathecal synthesis of borrelia-specific antibodies, and non-atherosclerotic pathology of brain supplying arteries. Vasculitic changes were detected by digital subtraction angiography, MR angiography and/or transcranial Doppler ultrasound. Outcomes were measured by the modified Rankin scale (mRS) and EuroQoL Index. Cerebral vasculitis is a rare complication of Lyme disease (0.3 % of all cases in the endemic area). Ten out of 11 patients diagnosed with neuroborreliosis-associated vasculitis cerebral vasculitis using clinical, radiological and immunological criteria developed ischemic stroke or transient ischemic attacks (TIA), 7 patients had recurrent stroke. Vasculitic alterations could be demonstrated in 8 patients that all except one developed ischemic lesions. The median mRS was 3 (range 0–4) at admission and 2 (range 0–6) at discharge. The posterior circulation was affected in 8 of 11 patients; thrombosis of the basilar artery was detected in 2 patients, one died in the acute stage. Neuroborreliosis can cause recurrent stroke or TIA on the basis of cerebral vasculitis. Lumbar puncture is needed for detection of this potentially life-threatening condition. Early recognition and adequate therapy would possibly improve outcome.
18F-FDG-Based Radiomics and Machine Learning Godefroy, Thomas; Frécon, Gauthier; Asquier-Khati, Antoine ...
JACC. Cardiovascular imaging,
July 2023, 2023-07-00, Letnik:
16, Številka:
7
Journal Article
Recenzirano
Fluorine-18 fluorodeoxyglucose (18F-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.
The primary objective of this study was to evaluate the performance of a radiomics and machine learning–based analysis of 18F-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.
All 18F-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.
A total of 108 patients were included, regardless of myocardial uptake, and were divided into training (n = 68) and test (n = 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%.
The use of ML for analyzing 18F-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 18F-FDG PET/CT in PVE diagnosis.
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BACKGROUNDFluorine-18 fluorodeoxyglucose (18F-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. OBJECTIVESThe primary objective of this study was to evaluate the performance of a radiomics and machine learning-based analysis of 18F-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. METHODSAll 18F-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. RESULTSA total of 108 patients were included, regardless of myocardial uptake, and were divided into training (n = 68) and test (n = 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%. CONCLUSIONSThe use of ML for analyzing 18F-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 18F-FDG PET/CT in PVE diagnosis.
Fluorine-18 fluorodeoxyglucose (
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.
The primary objective of this study was to evaluate the performance of a radiomics and machine learning-based analysis of
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.
All
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.
A total of 108 patients were included, regardless of myocardial uptake, and were divided into training (n = 68) and test (n = 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%.
The use of ML for analyzing
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
F-FDG PET/CT in PVE diagnosis.