Re: Accuracy of hepatocellular carcinoma detection on multidetector CT in a transplant liver population with explant liver correlation.
Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK.Clinical radiology (Impact Factor: 1.65). 07/2011; DOI:10.1016/j.crad.2011.05.010
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ABSTRACT: OBJECTIVE: To examine if liver stiffness measured by magnetic resonance elastography (MRE) is a risk factor for hepatocellular carcinoma (HCC) in patients with chronic liver disease. METHODS: By reviewing the records of magnetic resonance (MR) examinations performed at our institution, we selected 301 patients with chronic liver disease who did not have a previous medical history of HCC. All patients underwent MRE and gadoxetic acid-enhanced MR imaging. HCC was identified on MR images in 66 of the 301 patients, who were matched to controls from the remaining patients without HCC according to age. MRE images were obtained by visualising elastic waves generated in the liver by pneumatic vibration transferred via a cylindrical passive driver. Risk factors of HCC development were determined by the odds ratio with logistic regression analysis; gender and liver stiffness by MRE and serum levels of aspartate transferase, alanine transferase, alpha-fetoprotein, and protein induced by vitamin K absence-II. RESULTS: Multivariate analysis revealed that only liver stiffness by MRE was a significant risk factor for HCC with an odds ratio (95 % confidence interval) of 1.38 (1.05-1.84). CONCLUSION: Liver stiffness measured by MRE is an independent risk factor for HCC in patients with chronic liver disease. KEY POINTS: • Magnetic resonance elastography can estimate liver stiffness, a marker of hepatic fibrosis. • Liver stiffness is an independent risk factor of hepatocellular carcinoma (HCC). • Liver stiffness seems a better indicator of HCC than tumour markers.European Radiology 07/2012; · 3.55 Impact Factor
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ABSTRACT: PURPOSE: Our aim is to develop an automatic method which can detect diverse focal liver lesions (FLLs) in 3D CT volumes. METHOD: A hybrid generative-discriminative framework is proposed. It first uses a generative model to describe non-lesion components and then identifies all candidate FLLs within a 3D liver volume by eliminating non-lesion components. It subsequently uses a discriminative approach to suppress false positives with the advantage of tumoroid, a novel measurement combining three shape features spherical symmetry, compactness and size. RESULTS: This method was tested on 71 abdominal CT datasets (5,854 slices from 61 patients, with 261 FLLs covering six pathological types) and evaluated using the free-response receiver operating characteristic (FROC) curves. Overall, it achieved a true positive rate of 90 % with one false positive per liver. It degenerated gently with the decrease in lesion sizes to 30 ml. It achieved a true-positive rate of 36 % when tested on the lesions less than 4 ml. The average computing time of the lesion detection is 4 min and 28 s per CT volume on a PC with 2.67 GHz CPU and 4.0 GB RAM. CONCLUSIONS: The proposed method is comparable to the radiologists' visual investigation in terms of efficiency. The tool has great potential to reduce radiologists' burden in going through thousands of images routinely.International Journal of Computer Assisted Radiology and Surgery 03/2013; · 1.36 Impact Factor
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