Hypervascular Hepatic Focal Lesions: Spectrum of Imaging Features

Namasivayam, Saravanan; Salman, Khalil; Mittal, Pardeep K.; Martín, Diego R.; Small, William

doi:10.1067/j.cpradiol.2006.12.004

Cited by 32 publications

(28 citation statements)

References 14 publications

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“…The normal HVPG value is between 1 and 5 mm Hg, whereas HVPG ≥10 mm Hg is predictive of development of complications of cirrhosis, including death. 187 However, 1 study found no correlation between HVPG and the extent of pathological liver injury in Fontan patients.…”

Section: Diagnosismentioning

confidence: 90%

Congenital Heart Disease in the Older Adult

Bhatt¹,

Foster²,

Kuehl³

et al. 2015

Circulation

203

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Section: Diagnosismentioning

confidence: 90%

Congenital Heart Disease in the Older Adult

Bhatt¹,

Foster²,

Kuehl³

et al. 2015

Circulation

203

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“…However, many liver neoplasia, especially hepatocellular carcinomas (HCCs), are characterized by nearly exclusively arterial perfusion (1). Consequently, these tumors are usually arterially hypervascularized, compared with noncancerous liver tissue (2).…”

mentioning

confidence: 99%

Early Dynamic ¹⁸F-FDG PET to Detect Hyperperfusion in Hepatocellular Carcinoma Liver Lesions

et al. 2013

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In addition to angiographic data on vascularity and vascular access, demonstration of hepatocellular carcinoma (HCC) liver nodule hypervascularization is a prerequisite for certain intrahepatic antitumor therapies. Early dynamic (ED) 18 F-FDG PET/ CT could serve this purpose when the current standard method, contrast-enhanced (CE) CT, or other CE morphologic imaging modalities are unsuitable. A recent study showed ED 18 F-FDG PET/CT efficacy in this setting but applied a larger-thanstandard 18 F-FDG activity and an elaborate protocol likely to hinder routine use. We developed a simplified protocol using standard activities and easily generated visual and descriptive or quantitative endpoints. This pilot study assessed the ability of these endpoints to detect HCC hyperperfusion and, thereby, evaluated the suitability in of the protocol everyday practice. Methods: Twenty-seven patients with 34 HCCs (diameter $ 1.5 cm) with hypervascularization on 3-phase CE CT underwent liver ED 18 F-FDG PET for 240 s, starting with 18 F-FDG (250-MBq bolus injection). Four frames at 15-s intervals, followed by 3 frames at 60-s intervals were reconstructed. Endpoints included focal tracer accumulation in the first 4 frames (60 s), subsequent focal washout, and visual and quantitative differences between tumor and liver regions of interest in maximum and mean ED standardized uptake value (ED SUVmax and ED SUVmean, respectively) 240-s time-activity curves. Results: All 34 lesions were identified by early focal 18 F-FDG accumulation and faster time-to-peak ED SUVmax or ED SUVmean than in nontumor tissue. Tumor peak ED SUVmax and ED SUVmean exceeded liver levels in 85% and 53%, respectively, of lesions. Nadir tumor signal showed no consistent pattern relative to nontumor signal. HCC had a significantly shorter time to peak and significantly faster rate to peak for both ED SUVmax and ED SUVmean curves and a significantly higher peak ED SUVmax but not peak ED SUVmean than the liver. Conclusion: This pilot study provided proof of principle that our simplified ED 18 F-FDG PET/CT protocol includes endpoints that effectively detect HCC hypervascularization; this finding suggests that the protocol can be used routinely.

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“…Differential diagnosis in patients with focal liver lesions (FLLs) using B-mode ultrasound (US) images is broad due to the existence of a wide variety of sonographic appearances even with-in individual classes of FLLs [1][2][3][4]. Even then, B-mode US is the first choice for characterization of FLLs mainly due to its non-radioactive, non-invasive, inexpensive nature and real time imaging capabilities [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Even then, B-mode US is the first choice for characterization of FLLs mainly due to its non-radioactive, non-invasive, inexpensive nature and real time imaging capabilities [5][6][7]. In comparison to B-mode US, other imaging modalities like, contrast enhanced US, contrast enhanced spiral computed tomography (CT) and magnetic resonance imaging (MRI) offer high sensitivity for characterization of FLLs, but these imaging modalities are expensive, pose greater operational inconvenience and are not widely available [1,5,6,[8][9][10][11][12][13]. Therefore, an efficient computeraided classification (CAD) system for classification of FLLs based on conventional gray scale B-mode US is highly desired.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound

et al. 2014

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A neural network ensemble (NNE) based computeraided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.

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Hypervascular Hepatic Focal Lesions: Spectrum of Imaging Features

Cited by 32 publications

References 14 publications

Congenital Heart Disease in the Older Adult

Congenital Heart Disease in the Older Adult

Early Dynamic ¹⁸F-FDG PET to Detect Hyperperfusion in Hepatocellular Carcinoma Liver Lesions

Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound

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Hypervascular Hepatic Focal Lesions: Spectrum of Imaging Features

Cited by 32 publications

References 14 publications

Congenital Heart Disease in the Older Adult

Congenital Heart Disease in the Older Adult

Early Dynamic 18F-FDG PET to Detect Hyperperfusion in Hepatocellular Carcinoma Liver Lesions

Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound

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Product

Resources

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Early Dynamic ¹⁸F-FDG PET to Detect Hyperperfusion in Hepatocellular Carcinoma Liver Lesions