Fluorine-18 fluorodeoxyglucose positron emission tomography-based textural features for prediction of event prone carotid atherosclerotic plaques

BACKGROUND

Texture analysis has been increasingly used in the field of positron emission tomography (PET)/computed tomography (CT) imaging with Fluorine-18 fluorodeoxyglucose (18F-FDG), aiming at assessing tumor heterogeneity. The purpose of the present study is to examine the feasibility of performing texture analysis in carotid arteries, investigate the value of textural features as predictors of potential plaque vulnerability using as reference standards histological and immunohistochemical data and compare their performance with conventional uptake measurements.

METHODS

67 different 18F-FDG PET-based textural features were extracted from carotid images of 21 patients with high-grade carotid stenosis undergoing endarterectomy. To identify the more reliable predictors, univariate logistic regression analysis was performed. The accuracy was satisfactory in case of an Area Under the Receiver Operating Characteristic (ROC) curve (AUC) ≥ 0.80.

RESULTS

First measure of information correlation (AUC = 0.87, P < 0.001), large zone low gray level emphasis (AUC = 0.87, P < 0.001), and normalized run length non-uniformity (AUC = 0.84, P < 0.001) were the most optimal textural features for identifying characteristics of plaque vulnerability based on histological analysis. Addition of textural features to target-to-background ratio (TBR) (AUC = 0.74, P = 0.031) resulted in an AUC = 0.92 (P < 0.001), however, this did not reach statistical significance (Pdiff = 0.09). Intensity histogram standard deviation (AUC = 0.87, P < 0.001) and joint variance (AUC = 0.81, P = 0.001) were the most efficient features for signal differential in relation to immunohistochemical findings and provided incremental value compared to TBR (Pdiff = 0.02).

CONCLUSION

Texture analysis can be applied in 18F-FDG PET carotid imaging providing valuable information for plaque characterization.

Lymphoma Severity and Type Are Associated With Aortic FDG Uptake by 18F-FDG PET/CT Imaging

Background

There is evidence that metabolic disease burden in lymphoma influences patient outcome. However, the impact of disease severity on the cardiovascular system is unknown.

Objectives

The aim of this study was to examine whether lymphoma is associated with arterial inflammation by investigating the relationship between disease metabolic burden and arterial fluorodeoxyglucose (FDG) uptake.

Methods

Sixty-two chemotherapy-naïve patients with active Hodgkin’s or non-Hodgkin’s lymphoma were matched (2:1) to individual control groups of lymphoma patients previously treated and free of active disease. All groups underwent ¹⁸F-FDG position emission tomography–computed tomography imaging. Disease severity was quantified by metabolic tumor volume (MTV) and total lesion glycolysis corresponding to standardized uptake values (SUVs) ≥41% or ≥2.5 of the maximum SUV within lymphoma regions, and aortic FDG uptake was quantified through the target-to-background ratio (TBR). Inflammatory and disease severity biomarkers were also measured.

Results

MTV and total lesion glycolysis measurements were significantly correlated with inflammatory and disease biomarkers. Aortic TBR was higher in patients with active non-Hodgkin’s lymphoma compared with control subjects (median difference 0.51; 95% confidence interval [CI]: 0.28 to 0.78; p < 0.001). Similarly, patients with active Hodgkin’s lymphoma had higher values of aortic TBR compared with control subjects (median difference 0.31; 95% CI: 0.15 to 0.49; p < 0.001). In addition, aortic TBR was modestly increased in patients with stage III to IV disease compared with those with stage I to II disease (median aortic TBR: 2.23 [interquartile range: 2.01 to 2.54] vs. 2.06 [interquartile range: 1.83 to 2.27; p = 0.050). In multivariable analysis, aortic FDG uptake and MTV_≥2.5 values were independently associated (β = 0.425; 95% CI: 0.189 to 0.662; p = 0.001; R² = 0.208), as were aortic FDG uptake and MTV_≥41% (β = 0.407; 95% CI: 0.167 to 0.649, p = 0.001; R² = 0.191).

Conclusions

Aortic wall FDG uptake is related with disease severity indicative of a possible vascular effect of lymphoma. This work highlights a new potential role of molecular imaging in cardio-oncology for evaluating disease severity and its consequences on the vasculature.

Adjustment of vascular 2-deoxy-2-[18F]fluoro-D-glucose uptake values over time through a modeling approach

To develop and test a model predicting 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) standardized uptake value (SUV) changes over time in the aorta and the superior vena cava (SVC). Maximum aortic SUV and mean SVC SUV were determined at two time points (T1 and T2) in the ascending (ASC), descending (DSC), abdominal (ABD) aorta, aortic arch (ARC) and SVC of patients who have undergone [18F]FDG PET/CT for clinical purposes. For SUV prediction at T2, linear and non-linear models of SUV difference for a given time change were developed in a derivation group. The results were tested in an independent validation group, whilst model reproducibility was tested in patients of the validation group who have undergone a second clinically indicated scan. Applying the linear model in the derivation group, there were no statistically significant differences in measurements obtained in the examined segments: mean differences ranged from 0 ± 0.10 in SVC to 0.01 ± 0.13 in ARC between measured and predicted SUV. In contrast, in the non-linear model, there were statistically significant differences in measurements, except in ARC, with mean differences ranging from 0.04 ± 0.14 in ARC to 0.28 ± 0.13 in ABD. In the validation group using the linear model, there were no statistically significant differences, with mean differences ranging from - 0.01 ± 0.08 in ASC to - 0.03 ± 0.11 in ABD. Regarding reproducibility, mean differences were no statistically significant, ranging from 0.004 ± 0.06 in ASC to - 0.02 ± 0.16 in ABD. We have developed a linear model allowing accurate and reproducible prediction of SUV changes over time in the aorta and SVC.