Liver Computed Tomographic Perfusion in the Assessment of Microvascular Invasion in Patients With Small Hepatocellular Carcinoma

Wu, Dong; Tan, Ming; Zhou, Meiling; Sun, Hui‐Chuan; Ji, Yuan; Chen, Lingli; Chen, Gang; Zeng, Mengsu

doi:10.1097/rli.0000000000000098

Cited by 35 publications

(23 citation statements)

References 46 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such, in order to facilitate its inclusion into the selection process, a wide variety of studies are focused on optimizing methods of preoperative prediction of microvascular invasion using imaging modalities and other pre-transplant factors27282930404142. The results of the present study confirm that a combination of morphological tumor characteristics with serum alpha-fetoprotein concentration may be used for this purpose with moderate accuracy.…”

Section: Discussionsupporting

confidence: 57%

Limitations of predicting microvascular invasion in patients with hepatocellular cancer prior to liver transplantation

Grąt

Stypułkowski

Patkowski

et al. 2017

Sci Rep

View full text Add to dashboard Cite

Microvascular invasion (MVI) is well known to negatively influence outcomes following surgical treatment of hepatocellular cancer (HCC) patients. The aim of this study was to evaluate the rationale for prediction of MVI before liver transplantation (LT). Data of 200 HCC patients after LT were subject to retrospective analysis. MVI was present in 57 patients (28.5%). Tumor number (p = 0.001) and size (p = 0.009), and alpha-fetoprotein (p = 0.049) were independent predictors of MVI used to create a prediction model, defined as: 0.293x(tumor number) + 0.283x(tumor size in cm) + 0.164xloge(alpha-fetoprotein in ng/ml) (c statistic = 0.743). The established cut-off (≥2.24) was associated with sensitivity and specificity of 72%. MVI was not an independent risk factor for recurrence (p = 0.307), in contrast to tumor number (p = 0.047) and size (p < 0.001), alpha-fetoprotein (p < 0.001) and poor differentiation (p = 0.039). Recurrence-free survival at 5 years for patients without MVI was 85.9% as compared to 83.3% (p = 0.546) and 55.3% (p = 0.001) for patients with false negative and true positive prediction of MVI, respectively. The use of both morphological and biological tumor features enables effective pre-transplant prediction of high-risk MVI. Provided that these parameters are combined in selection of HCC patients for LT, pre-transplant identification of all patients with MVI does not appear necessary.

show abstract

Section: Discussionsupporting

confidence: 57%

Limitations of predicting microvascular invasion in patients with hepatocellular cancer prior to liver transplantation

Grąt

Stypułkowski

Patkowski

et al. 2017

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Besides biological features and genetic profiles, 12 perfusion-based imaging is increasingly being used to assess the number and extension of hypervascularized lesions as well as the achieved postprocedural devascularization. [13][14][15][16][17] Besides hepatic perfusion index, blood flow, and k-trans, hepatic blood volume seems to be a useful parameter in the detailed and quantitative evaluation of the degree of HCC vascularization. Chen et al 18 evaluated the value of VPCT in the posttreatment assessment of HCC lesions undergoing cTACE.…”

Section: Discussionmentioning

confidence: 99%

Parenchymal Blood Volume Assessed by C-Arm–Based Computed Tomography in Immediate Posttreatment Evaluation of Drug-Eluting Bead Transarterial Chemoembolization in Hepatocellular Carcinoma

Syha

Grözinger

Grosse³

et al. 2016

Investigative Radiology

View full text Add to dashboard Cite

show abstract

“…Therefore, some rapidly changing parameters, such as the peak arterial enhancement might be displayed partially incorrect which could falsify the calculated perfusion parameters. However, this could be avoided by using a scanner with a wider detector [31]. ROIs were used for analysis, which does not represent the lesion's entire parameter-map and may lead to the following biases: As two observers manually placed the ROIs in the CT source images, it is possible that they were placed in different lesion areas, leading to a bias in the following data analysis, as the automatic placement of the following ROIs in the parameter maps would also be misplaced.…”

Section: Limitationsmentioning

confidence: 99%

Comparison of different 4D CT-Perfusion algorithms to visualize lesions after microwave ablation in an in vivo porcine model

Bressem

Vahldiek

Erxleben

et al. 2019

International Journal of Hyperthermia

View full text Add to dashboard Cite

Background: Accurate lesion visualization after microwave ablation (MWA) remains a challenge. Computed tomography perfusion (CTP) has been proposed to improve visualization, but it was shown that different perfusion-models delivered different results on the same data set. Purpose: Comparison of different perfusion algorithms and identification of the algorithm enables for the best imaging of lesion after hepatic MWA. Materials and methods: 10 MWA with consecutive CTP were performed in healthy pigs. Parametermaps were generated using a single-input-dual-compartment-model with Patlak's algorithm (PM), a dual-input-maximum-slope-model (DIMS), a dual-input-one-compartment-model (DIOC), a single-(SIDC) and dual-input-deconvolution-model (DIDC). Parameter-maps for hepatic arterial (AF) and portal venous blood flow (PF), mean transit time, hepatic blood volume (HBV) and capillary permeability were compared regarding the values of the normal liver tissue (NLT), lesion, contrast-and signal-to-noise ratios (SNR, CNR) and inter-and intrarater-reliability using the intraclass correlation coefficient, Bland-Altman plots and linear regression. Results: Perfusion values differed between algorithms with especially large fluctuations for the DIOC. A reliable differentiation of lesion margin appears feasible with parameter-maps of PF and HBV for most algorithms, except for the DIOC due to large fluctuations in PF. All algorithms allowed for a demarcation of the central necrotic zone based on hepatic AF and HBV. The DIDC showed the highest CNR and the best inter-and intrarater reliability. Conclusion: The DIDC appears to be the most feasible model to visualize margins and necrosis zones after microwave ablation, but due to high computational demand, a single input deconvolution algorithm might be preferable in clinical practice.

show abstract

Liver Computed Tomographic Perfusion in the Assessment of Microvascular Invasion in Patients With Small Hepatocellular Carcinoma

Abstract: Liver CT perfusion provides a noninvasive, quantitative method that can predict mVI in patients with sHCC through measurement of 3 perfusion parameters: PVFtumor, ΔPVF, and rPVF.

Cited by 35 publications

References 46 publications

Limitations of predicting microvascular invasion in patients with hepatocellular cancer prior to liver transplantation

Limitations of predicting microvascular invasion in patients with hepatocellular cancer prior to liver transplantation

Parenchymal Blood Volume Assessed by C-Arm–Based Computed Tomography in Immediate Posttreatment Evaluation of Drug-Eluting Bead Transarterial Chemoembolization in Hepatocellular Carcinoma

Comparison of different 4D CT-Perfusion algorithms to visualize lesions after microwave ablation in an in vivo porcine model

Contact Info

Product

Resources

About