Publications by authors named "Lisa Duff"
Article Synopsis
- The study investigates the effectiveness of [F]FDG PET and LGE-CMR imaging methods for distinguishing cardiac sarcoidosis (CS) from myocardial inflammation resulting from COVID-19.
- It involved analyzing image data from 35 post-COVID-19 patients and 40 CS patients, focusing on extracting and testing radiomic features to improve diagnostic accuracy for these conditions.
- The results indicated that the combined signature of features from [F]FDG PET performed exceptionally well with an AUC of 0.98 and accuracy of 0.91, while LGE-CMR achieved an accuracy of 0.75 and AUC of 0.87, highlighting the potential of machine learning classifiers in enhancing diagnostic outcomes.
Background: Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary.
View Article and Find Full Text PDFBackground: The aim of this study is to explore the utility of cardiac magnetic resonance (CMR) imaging of radiomic features to distinguish active and inactive cardiac sarcoidosis (CS).
Methods: Subjects were classified into active cardiac sarcoidosis (CS) and inactive cardiac sarcoidosis (CS) based on PET-CMR imaging. CS was classified as featuring patchy [F]fluorodeoxyglucose ([F]FDG) uptake on PET and presence of late gadolinium enhancement (LGE) on CMR, while CS was classified as featuring no [F]FDG uptake in the presence of LGE on CMR.
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.
View Article and Find Full Text PDFBackground: The aim of this study was to explore the feasibility of assisted diagnosis of active (peri-)aortitis using radiomic imaging biomarkers derived from [F]-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.
Background: This study aimed to explore the radiomic features from PET images to detect active cardiac sarcoidosis (CS).
Methods: Forty sarcoid patients and twenty-nine controls were scanned using FDG PET-CMR. Five feature classes were compared between the groups.