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Kinetic analysis of dynamic (11)C-acetate PET/CT imaging as a potential method for differentiation of hepatocellular carcinoma and benign liver lesions.

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TLDR
11C-acetate kinetic parameter K1 could improve the identification of HCC from benign lesions in combination with T/L in discriminant analysis and appears to be a very helpful method for clinical liver masses diagnosis and staging.
Abstract
Objective: The kinetic analysis of 11C-acetate PET provides more information than routine one time-point static imaging. This study aims to investigate the potential of dynamic 11C-acetate hepatic PET imaging to improve the diagnosis of hepatocellular carcinoma (HCC) and benign liver lesions by using compartmental kinetic modeling and discriminant analysis. Methods: Twenty-two patients were enrolled in this study, 6 cases were with well-differentiated HCCs, 7 with poorly-differentiated HCCs and 9 with benign pathologies. Following the CT scan, all patients underwent 11C-acetate dynamic PET imaging. A three-compartment irreversible dual-input model was applied to the lesion time activity curves (TACs) to estimate the kinetic rate constants K1-k3, vascular fraction (VB) and the coefficient α representing the relative hepatic artery (HA) contribution to the hepatic blood supply on lesions and non-lesion liver tissue. The parameter Ki (=K1×k3/(k2 + k3)) was calculated to evaluate the local hepatic metabolic rate of acetate (LHMAct). The lesions were further classified by discriminant analysis with all the above parameters. Results: K1 and lesion to non-lesion standardized uptake value (SUV) ratio (T/L) were found to be the parameters best characterizing the differences among well-differentiated HCC, poorly-differentiated HCC and benign lesions in stepwise discriminant analysis. With discriminant functions consisting of these two parameters, the accuracy of lesion prediction was 87.5% for well-differentiated HCC, 50% for poorly-differentiated HCC and 66.7% for benign lesions. The classification was much better than that with SUV and T/L, where the corresponding classification accuracy of the three kinds of lesions was 57.1%, 33.3% and 44.4%. Conclusion: 11C-acetate kinetic parameter K1 could improve the identification of HCC from benign lesions in combination with T/L in discriminant analysis. The discriminant analysis using static and kinetic parameters appears to be a very helpful method for clinical liver masses diagnosis and staging.

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Diagnostic and therapeutic management of hepatocellular carcinoma.

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Determination of a pharmacokinetic model for [11C]-acetate in brown adipose tissue.

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11C-acetate and 18F-fluorodeoxyglucose positron emission tomography/computed tomography dual imaging for the prediction of response and prognosis after transarterial chemoembolization.

TL;DR: Dual radiotracer use of 11C-acetate and 18F-FDG PET/CT contributed to the prediction of response and recurrence after TACE, and treatment plans could be more personalized and optimized.
References
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Partial-Volume Effect in PET Tumor Imaging

TL;DR: What PVE is and its consequences in PET tumor imaging are described; the parameters on which PVE depends are reviewed; and actions that can be taken to reduce the errors attributable to PVE are described.
Journal ArticleDOI

Benign hepatic tumours and tumour like conditions in men.

TL;DR: The results indicate that benign hepatic tumours and tumour like conditions are not rare in men but may remain undetected because of their small size.
Journal Article

11C-Acetate PET Imaging in Hepatocellular Carcinoma and Other Liver Masses

TL;DR: Histopathologic correlation suggests that (11)C-Acetate has a high sensitivity and specificity as a radiotracer complementary to (18)F-FDG in PET imaging of HCC and evaluation of other liver masses and dual-tracer uptake by different parts of the tumor is demonstrated.
Journal ArticleDOI

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

TL;DR: When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests.
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