Feasibility of perfusion and early-uptake 18F-FDG PET/CT in primary hepatocellular carcinoma: a dual-input dual-compartment uptake model

Wang, Shaobo; Li, Boqiao; Li, Pengfei; Rong, Xie; Wang, Quanshi; Shi, Hong; He, Jianfeng

doi:10.1007/s11604-021-01140-6

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…This paper selects SUVmax to form the TAC for dynamics modeling. According to our clinical practice and prior literature, SUVmax has better repeatability than SUVmean because it does not depend heavily on the delineation of the ROI [ 30 – 32 ]. Additionally, SUVmax changed greatly in the first 1 min and then fluctuated slightly and became flat (Fig.…”

Section: Discussionmentioning

confidence: 99%

Short-term PET-derived kinetic estimation for the diagnosis of hepatocellular carcinoma: a combination of the maximum-slope method and dual-input three-compartment model

Wang

Shi

et al. 2023

Insights Imaging

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Background Kinetic estimation provides fitted parameters related to blood flow perfusion and fluorine-18-fluorodeoxyglucose (18F-FDG) transport and intracellular metabolism to characterize hepatocellular carcinoma (HCC) but usually requires 60 min or more for dynamic PET, which is time-consuming and impractical in a busy clinical setting and has poor patient tolerance. Methods This study preliminarily evaluated the equivalence of liver kinetic estimation between short-term (5-min dynamic data supplemented with 1-min static data at 60 min postinjection) and fully 60-min dynamic protocols and whether short-term 18F-FDG PET-derived kinetic parameters using a three-compartment model can be used to discriminate HCC from the background liver tissue. Then, we proposed a combined model, a combination of the maximum-slope method and a three-compartment model, to improve kinetic estimation. Results There is a strong correlation between the kinetic parameters K1 ~ k3, HPI and $${{\varvec{V}}}_{{\varvec{b}}}$$ V b in the short-term and fully dynamic protocols. With the three-compartment model, HCCs were found to have higher k2, HPI and k3 values than background liver tissues, while K1, k4 and $${{\varvec{V}}}_{{\varvec{b}}}$$ V b values were not significantly different between HCCs and background liver tissues. With the combined model, HCCs were found to have higher HPI, K1 and k2, k3 and $${{\varvec{V}}}_{{\varvec{b}}}$$ V b values than background liver tissues; however, the k4 value was not significantly different between HCCs and the background liver tissues. Conclusions Short-term PET is closely equivalent to fully dynamic PET for liver kinetic estimation. Short-term PET-derived kinetic parameters can be used to distinguish HCC from background liver tissue, and the combined model improves the kinetic estimation. Clinical relevance statement Short-term PET could be used for hepatic kinetic parameter estimation. The combined model could improve the estimation of liver kinetic parameters. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Short-term PET-derived kinetic estimation for the diagnosis of hepatocellular carcinoma: a combination of the maximum-slope method and dual-input three-compartment model

Wang

Shi

et al. 2023

Insights Imaging

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“…The SUVmax was measured by delineating regions of interest (ROIs) on PET images following the methodology described in previous studies [11,25]. Briefly, for lesions with imperceptible FDG uptake, ROIs were drawn relative to the conventional imaging findings.…”

Section: Image Analysismentioning

confidence: 99%

Kinetic 18F-FDG PET/CT imaging of hepatocellular carcinoma: a dual input four-compartment model

Wang,

Deng,

Wang

et al. 2024

EJNMMI Phys

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Background The endoplasmic reticulum plays an important role in glucose metabolism and has not been explored in the kinetic estimation of hepatocellular carcinoma (HCC) via 18F-fluoro-2-deoxy-d-glucose PET/CT. Methods A dual-input four-compartment (4C) model, regarding endoplasmic reticulum was preliminarily used for kinetic estimation to differentiate 28 tumours from background liver tissue from 24 patients with HCC. Moreover, parameter images of the 4C model were generated from one patient with negative findings on conventional metabolic PET/CT. Results Compared to the dual-input three-compartment (3C) model, the 4C model has better fitting quality, a close transport rate constant (K1) and a dephosphorylation rate constant (k6/k4), and a different removal rate constant (k2) and phosphorylation rate constant (k3) in HCC and background liver tissue. The K1, k2, k3, and hepatic arterial perfusion index (HPI) from the 4C model and k3, HPI, and volume fraction of blood (Vb) from the 3C model were significantly different between HCC and background liver tissues (all P < 0.05). Meanwhile, the 4C model yielded additional kinetic parameters for differentiating HCC. The diagnostic performance of the top ten genes from the most to least common was HPI(4C), Vb(3C), HPI(3C), SUVmax, k5(4C), k3(3C), k2(4C), v(4C), K1(4C) and Vb(4C). Moreover, a patient who showed negative findings on conventional metabolic PET/CT had positive parameter images in the 4C model. Conclusions The 4C model with the endoplasmic reticulum performed better than the 3C model and produced additional useful parameters in kinetic estimation for differentiating HCC from background liver tissue.

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“…PET also estimates liver function based on the uptake and clearance of different radioactive positron-emitting tracers (e.g., 18 F FDGal) (Bak-Fredslund et al, 2017 ; Keiding et al, 2018 ) and has been applied to predict postoperative liver function (Cho et al, 2017 ). Multi compartment models are employed to derive further tissue parameters, like hepatic arterial or portal blood flow, hepatic arterial or portal perfusion index or blood volume, from the dynamic time course of the contrast agent passing through the tissue (Wang et al, 2021a ).…”

Section: Clinical and Experimental Partmentioning

confidence: 99%

Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function

et al. 2021

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Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.

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Feasibility of perfusion and early-uptake 18F-FDG PET/CT in primary hepatocellular carcinoma: a dual-input dual-compartment uptake model

Cited by 9 publications

References 32 publications

Short-term PET-derived kinetic estimation for the diagnosis of hepatocellular carcinoma: a combination of the maximum-slope method and dual-input three-compartment model

Short-term PET-derived kinetic estimation for the diagnosis of hepatocellular carcinoma: a combination of the maximum-slope method and dual-input three-compartment model

Kinetic 18F-FDG PET/CT imaging of hepatocellular carcinoma: a dual input four-compartment model

Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function

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