Dataset Information

18F-Fluoride Signal Amplification Identifies Microcalcifications Associated With Atherosclerotic Plaque Instability in Positron Emission Tomography/Computed Tomography Images.

ABSTRACT: BACKGROUND:Microcalcifications in atherosclerotic plaques are destabilizing, predict adverse cardiovascular events, and are associated with increased morbidity and mortality.18F-fluoride positron emission tomography (PET)/computed tomography (CT) imaging has demonstrated promise as a useful clinical diagnostic tool in identifying high-risk plaques; however, there is confusion as to the underlying mechanism of signal amplification seen in PET-positive, CT-negative image regions. This study tested the hypothesis that 18F-fluoride PET/CT can identify early microcalcifications. METHODS:18F-fluoride signal amplification derived from microcalcifications was validated against near-infrared fluorescence molecular imaging and histology using an in vitro 3-dimensional hydrogel collagen platform, ex vivo human specimens, and a mouse model of atherosclerosis. RESULTS:Microcalcification size correlated inversely with collagen concentration. The 18F-fluoride ligand bound to microcalcifications formed by calcifying vascular smooth muscle cell derived extracellular vesicles in the in vitro 3-dimensional collagen system and exhibited an increasing signal with an increase in collagen concentration (0.25 mg/mL collagen -33.8×102±12.4×102 counts per minute; 0.5 mg/mL collagen -67.7×102±37.4×102 counts per minute; P=0.0014), suggesting amplification of the PET signal by smaller microcalcifications. We further incubated human atherosclerotic endarterectomy specimens with clinically relevant concentrations of 18F-fluoride. The 18F-fluoride ligand labeled microcalcifications in PET-positive, CT-negative regions of explanted human specimens as evidenced by 18F-fluoride PET/CT imaging, near-infrared fluorescence, and histological analysis. Additionally, the 18F-fluoride ligand identified micro and macrocalcifications in atherosclerotic aortas obtained from low-density lipoprotein receptor-deficient mice. CONCLUSIONS:Our results suggest that 18F-fluoride PET signal in PET-positive, CT-negative regions of human atherosclerotic plaques is the result of developing microcalcifications, and high surface area in regions of small microcalcifications may amplify PET signal.

SUBMITTER: Creager MD

PROVIDER: S-EPMC6338081 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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<sup>18</sup>F-Fluoride Signal Amplification Identifies Microcalcifications Associated With Atherosclerotic Plaque Instability in Positron Emission Tomography/Computed Tomography Images.

Creager Michael D MD Hohl Tobias T Hutcheson Joshua D JD Moss Alastair J AJ Schlotter Florian F Blaser Mark C MC Park Mi-Ae MA Lee Lang Ho LH Singh Sasha A SA Alcaide-Corral Carlos J CJ Tavares Adriana A S AAS Newby David E DE Kijewski Marie F MF Aikawa Masanori M Di Carli Marcelo M Dweck Marc R MR Aikawa Elena E

Circulation. Cardiovascular imaging 20190101 1

<h4>Background</h4>Microcalcifications in atherosclerotic plaques are destabilizing, predict adverse cardiovascular events, and are associated with increased morbidity and mortality.<sup>18</sup>F-fluoride positron emission tomography (PET)/computed tomography (CT) imaging has demonstrated promise as a useful clinical diagnostic tool in identifying high-risk plaques; however, there is confusion as to the underlying mechanism of signal amplification seen in PET-positive, CT-negative image regions ...[more]

PMID: 30642216

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Project description:Vulnerable carotid atherosclerotic plaque (CAP) significantly contributes to ischemic stroke. Neovascularization within plaques is an emerging biomarker linked to plaque vulnerability that can be detected using contrast-enhanced ultrasound (CEUS). Computed tomography angiography (CTA) is a common method used in clinical cerebrovascular assessments that can be employed to evaluate the vulnerability of CAPs. Radiomics is a technique that automatically extracts radiomic features from images. This study aimed to identify radiomic features associated with the neovascularization of CAP and construct a prediction model for CAP vulnerability based on radiomic features. CTA data and clinical data of patients with CAPs who underwent CTA and CEUS between January 2018 and December 2021 in Beijing Hospital were retrospectively collected. The data were divided into a training cohort and a testing cohort using a 7:3 split. According to the examination of CEUS, CAPs were dichotomized into vulnerable and stable groups. 3D Slicer software was used to delineate the region of interest in CTA images, and the Pyradiomics package was used to extract radiomic features in Python. Machine learning algorithms containing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perception (MLP) were used to construct the models. The confusion matrix, receiver operating characteristic (ROC) curve, accuracy, precision, recall, and f-1 score were used to evaluate the performance of the models. A total of 74 patients with 110 CAPs were included. In all, 1,316 radiomic features were extracted, and 10 radiomic features were selected for machine-learning model construction. After evaluating several models on the testing cohorts, it was discovered that model_RF outperformed the others, achieving an AUC value of 0.93 (95% CI: 0.88-0.99). The accuracy, precision, recall, and f-1 score of model_RF in the testing cohort were 0.85, 0.87, 0.85, and 0.85, respectively. Radiomic features associated with the neovascularization of CAP were obtained. Our study highlights the potential of radiomics-based models for improving the accuracy and efficiency of diagnosing vulnerable CAP. In particular, the model_RF, utilizing radiomic features extracted from CTA, provides a noninvasive and efficient method for accurately predicting the vulnerability status of CAP. This model shows great potential for offering clinical guidance for early detection and improving patient outcomes.

Project description:Purpose [18F]Sodium fluoride (NaF) positron emission tomography (PET)/computed tomography (CT) is a promising radiotracer for quantitative assessment of bone metastases. This study assesses changes in early NaF PET/CT response measures in metastatic prostate cancer for correlation to clinical outcomes. Patients and Methods Fifty-six patients with metastatic castration-resistant prostate cancer (mCRPC) with osseous metastases had NaF PET/CT scans performed at baseline and after three cycles of chemotherapy (n = 16) or androgen receptor pathway inhibitors (n = 40). A novel technology, Quantitative Total Bone Imaging, was used for analysis. Global imaging metrics, including maximum standardized uptake value (SUVmax) and total functional burden (SUVtotal), were extracted from composite lesion-level statistics for each patient and tracked throughout treatment. Progression-free survival (PFS) was calculated as a composite end point of progressive events using conventional imaging and/or physician discretion of clinical benefit; NaF imaging was not used for clinical evaluation. Cox proportional hazards regression analyses were conducted between imaging metrics and PFS. Results Functional burden (SUVtotal) assessed midtreatment was the strongest univariable PFS predictor (hazard ratio, 1.97; 95% CI, 1.44 to 2.71; P < .001). Classification of patients based on changes in functional burden showed stronger correlation to PFS than did the change in number of lesions. Various global imaging metrics outperformed baseline clinical markers in predicting outcome, including SUVtotal and SUVmean. No differences in imaging response or PFS correlates were found for different treatment cohorts. Conclusion Quantitative total bone imaging enables comprehensive disease quantification on NaF PET/CT imaging, showing strong correlation to clinical outcomes. Total functional burden assessed after three cycles of hormonal therapy or chemotherapy was predictive of PFS for men with mCRPC. This supports ongoing development of NaF PET/CT-based imaging biomarkers in mCRPC to bone.

Dataset Information

18F-Fluoride Signal Amplification Identifies Microcalcifications Associated With Atherosclerotic Plaque Instability in Positron Emission Tomography/Computed Tomography Images.

Publications

<sup>18</sup>F-Fluoride Signal Amplification Identifies Microcalcifications Associated With Atherosclerotic Plaque Instability in Positron Emission Tomography/Computed Tomography Images.

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