Non-invasive evaluation method of the liver fibrosis using Real-time Tissue Elastography ∼Usefulness of judgment liver fibrosis stage by Liver Fibrosis Index (LF index)

研治, 藤本; 道夫, 加藤; 明子, 外村; 典久, 矢田; 千栄, 辰巳; 正秀, 尾下; 滋夫, 和田; 一臣, 上嶋; 哲士, 石田; 朋子, 古田; 大, 山崎; 正彦, 辻本; 満, 元木; 毅, 三竹; 栄浩, 金; 佳司, 山本; 毅, 椎名; 正俊, 工藤; 紀夫, 林

doi:10.2957/kanzo.51.539

Cited by 20 publications

(19 citation statements)

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“…However, liver biopsy results themselves have bias due to sampling error and due to the histopathological image being classified not by continuous quantity but staged by the progression of hepatic fibrosis. Moreover, there are large differences in the progression of hepatic fibrosis between 4 stages of liver fibrosis [40], thus the accuracy of liver biopsy is limited. Since liver biopsy results are used as a training set for our LFI computation, we speculate that there is also some bias in our estimate of liver fibrosis.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, in our previous report, we did not perform any investigation about the relationship between the grade of inflammation and the RTE image. Thus, the aims of this study are: (1) to present a newly developed image analysis software method to obtain Liver Fibrosis Index (LFI) automatically from image features of RTE images using multiple regression analysis [38,39,40], (2) to show the effectiveness of this software tool and (3) to establish the relationship between LFI, the stage of fibrosis and the grade of inflammation.…”

Section: Introductionmentioning

confidence: 99%

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Novel Image Analysis Method Using Ultrasound Elastography for Noninvasive Evaluation of Hepatic Fibrosis in Patients with Chronic Hepatitis C

Fujimoto¹,

Kato

Kudo

et al. 2013

Oncology

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It has been established that the long-term infection of chronic hepatitis C leads to the increased risk of hepatic fibrosis and hepatocellular carcinoma. Currently, histological diagnosis by invasive and painful liver biopsy is the gold standard for evaluating the hepatic fibrosis stage. Because of a side effect or patient inability to cope with the pain, it is difficult to assess the fibrosis stage frequently using liver biopsy. Recently, instead of liver biopsy, many articles have been published showing the usefulness of ultrasound elastography to evaluate the stage of hepatic fibrosis. We also reported the usefulness of real-time tissue elastography (RTE) for liver fibrosis staging in 2007. However, in our previous report, fibrosis classification was performed manually and the number of patients involved was also small. In the current study, the fibrosis staging is performed automatically using software by characterizing the elastography images. We have also increased the number of patients from 64 to 310. Thus, the aim of this study is to increase objectivity by using a newly developed automatic analysis method. We obtain the Liver Fibrosis Index (LFI), which is calculated from image features of RTE images, using multiple regression analysis performed on clinical data of 310 cases as the training data set. The correlation coefficient obtained between the LFI and the stage of hepatic fibrosis was r = 0.68, and significant differences exist between all stages of fibrosis (p < 0.001). Our new method seems promising since it has the ability to diagnose fibrosis even in the presence of inflammation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Image Analysis Method Using Ultrasound Elastography for Noninvasive Evaluation of Hepatic Fibrosis in Patients with Chronic Hepatitis C

Fujimoto¹,

Kato

Kudo

et al. 2013

Oncology

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“…1). To perform a quantitative evaluation, Liver Fibrosis Index (LFI) was calculated as follows: -0.009 × MEAN - 0.005 × SD + 0.023 × %AREA + 0.025 × COMP + 0.775 × SKEW - 0.281 × KURT + 2.083 × ENT + 3.042 × IDM + 39.979 × ASM - 5.542 [18,19]. The median LFI was calculated from 10 images.…”

Section: Methodsmentioning

confidence: 99%

Assessment of Liver Fibrosis with Real-Time Tissue Elastography in Chronic Viral Hepatitis

et al. 2013

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Objective: The aim of this study was to assess prospectively the accuracy of measurement of liver fibrosis with real-time tissue elastography (RTE) in patients with chronic viral hepatitis. Methods: Two hundred and forty-five patients were prospectively enrolled. Nine image features were measured from strain images, and Liver Fibrosis Index (LFI) was calculated from these features. Fibrosis stage was diagnosed from pathological specimens obtained by ultrasound-guided biopsy. LFI and serological markers were compared with pathological diagnosis, and the diagnostic performance of RTE was compared. Results: LFI in stages F0-F1, F2, F3 and F4 was 1.58, 2.03, 2.40 and 2.86, respectively, demonstrating a stepwise increase with increasing severity of liver fibrosis (p < 0.001). LFI in F2 did not significantly differ from that in F3, whereas for all other combinations of stages, there were significant differences. The area under the receiver operating characteristic curve of the LFI, platelet count, aspartate/alanine aminotransferase ratio, aspartate aminotransferase-to-platelet ratio, and FibroIndex for predicting F3 stage or higher (F0-F2 vs. F3-F4) was 0.865, 0.824, 0.708, 0.789 and 0.828, respectively. Conclusions: RTE is useful for diagnosis of liver fibrosis, regardless of stage, in patients with chronic viral hepatitis.

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“…From each RTE image, 11 image features were extracted: mean relative strain value (MEAN); standard deviation of relative strain value (SD); percentage of low strain area (percentage of the blue colored area; %AREA); complexity of low strain area (calculated as square of perimeter divided by area; COM); skewness (SKEW); kurtosis (KURT); contrast (CON); entropy (ENT); textural complexity, inverse difference moment (IDM); angular second moment (ASM), and correlation (COR). LFI was calculated using a previously reported method [17,18,19]. …”

Section: Methodsmentioning

confidence: 99%

“…A verification study using a mechanical model of liver fibrosis suggested that changes in RTE images associated with the progression of liver fibrosis are seen because RTE is actually depicting liver fibrosis itself [16]. In addition, the liver fibrosis index (LFI), which is a multiple regression equation for assessing liver fibrosis using liver fibrosis estimates based on biopsy and RTE data obtained from patients with CHC and cirrhosis, is widely used as a technique for assessing liver fibrosis with RTE as it can be easily measured with RTE systems [17,18,19,20]. However, the LFI was developed based on data from patients with CHC and cirrhosis, and its usefulness for other etiologies has not been sufficiently discussed.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Diagnosis of Liver Fibrosis: Utility of Data Mining of Both Ultrasound Elastography and Serological Findings to Construct a Decision Tree

et al. 2014

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Objective: Although liver biopsy is the gold standard for viral liver disease management, it is invasive and the sampling error rate is problematic. Real-time tissue elastography (RTE), a recently developed method of ultrasound elastography, can be used to assess liver fibrosis noninvasively but the overlap between fibrosis stages limits its ability to assess liver fibrosis adequately when used alone. Methods: A multicenter collaborative study involving 542 patients with chronic viral hepatitis and cirrhosis who were scheduled to undergo liver biopsy compared the image features obtained from RTE image analysis, the liver fibrosis index (LFI), and pathological diagnosis. RTE and a blood test were performed on the same day as the liver biopsy. Data mining was also performed to construct a decision tree, and its diagnostic performance for assessing liver fibrosis was evaluated. Results: The LFI was higher in patients with chronic hepatitis C (CHC) than in those with chronic hepatitis B (CHB). When a decision tree was constructed by data mining of RTE and serological findings, the diagnostic accuracy was very high for all fibrosis stages, with respective rates at F1, F2, F3, and F4 of 94.4, 54.1, 38.7, and 81.3% for patients with CHC and of 97.1, 50.0, 43.8, and 80.6% for patients with CHB. Conclusions: The variation in LFI values between the different etiologies appears to reflect the difference in the development style of liver fibrosis. The decision tree for assessing liver fibrosis constructed by data mining of both RTE and serological findings had a high diagnostic performance in assessing liver fibrosis and shows promising clinical utility.

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Non-invasive evaluation method of the liver fibrosis using Real-time Tissue Elastography ∼Usefulness of judgment liver fibrosis stage by Liver Fibrosis Index (LF index)

Cited by 20 publications

References 4 publications

Novel Image Analysis Method Using Ultrasound Elastography for Noninvasive Evaluation of Hepatic Fibrosis in Patients with Chronic Hepatitis C

Novel Image Analysis Method Using Ultrasound Elastography for Noninvasive Evaluation of Hepatic Fibrosis in Patients with Chronic Hepatitis C

Assessment of Liver Fibrosis with Real-Time Tissue Elastography in Chronic Viral Hepatitis

Noninvasive Diagnosis of Liver Fibrosis: Utility of Data Mining of Both Ultrasound Elastography and Serological Findings to Construct a Decision Tree

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