SU-E-U-01: Automatic Quantitative Analysis of Chronic Liver Disease Employing Shear Wave Ultrasound Elastography

Authors

  • Gatos I,

    1. University of Patras, Rion, Ahaia
    2. Technological Educational Institute of Athens, Egaleo, Attika
    3. Diagnostic Echotomography SA, Kifissia, Attika
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  • Tsantis S,

    1. University of Patras, Rion, Ahaia
    2. Technological Educational Institute of Athens, Egaleo, Attika
    3. Diagnostic Echotomography SA, Kifissia, Attika
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  • Skouroliakou A,

    1. University of Patras, Rion, Ahaia
    2. Technological Educational Institute of Athens, Egaleo, Attika
    3. Diagnostic Echotomography SA, Kifissia, Attika
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  • Theotokas I,

    1. University of Patras, Rion, Ahaia
    2. Technological Educational Institute of Athens, Egaleo, Attika
    3. Diagnostic Echotomography SA, Kifissia, Attika
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  • Zoumpoulis P,

    1. University of Patras, Rion, Ahaia
    2. Technological Educational Institute of Athens, Egaleo, Attika
    3. Diagnostic Echotomography SA, Kifissia, Attika
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  • Kagadis G

    1. University of Patras, Rion, Ahaia
    2. Technological Educational Institute of Athens, Egaleo, Attika
    3. Diagnostic Echotomography SA, Kifissia, Attika
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Abstract

Purpose:

The purpose of this study was to quantify liver elastic heterogeneity in Shear Wave Elastography (SWE) by using textural features and evaluating their diagnostic performance on differentiating healthy from chronic liver disease patients, taking biopsy results as the gold standard.

Methods:

Clinical material includes 16 healthy (F0) and 15 with Chronic Liver Disease (F1,F2,F3,F4) patients according to the Metavir staging system. All exams were performed using the Aixplorer ultrasound system with a SuperCurved SC6-1 transducer. From the SWE-QBox the RGB displayed elasticity data of Young's modulus were transformed from RGB color space into an elasticity matrix of gray tones, whose values varied from zero to the maximum elasticity measurement. Every pixel with no RGB-values was set to ‘−1’ due to non-valid elasticity value for that pixel. From the elastogram map 185 textural features were computed (5 from the gray-tone histogram, 26 second order statistic features, extracted from the co-occurrence matrices and 10 features extracted from the run-length matrices over four directions (00,450,900,1350) and distances of (d=1,3,5,7,9) pixels). Stepwise multi-linear regression analysis was utilized to avoid feature redundancy leading to a feature subset feeding a Support Vector Machine (SVM) classifier. SVM-model evaluation was performed by means of the leave-one-out method.

Results:

Maximum classification accuracy (93.5%) in distinguishing healthy from chronic liver disease patients was obtained employing three textural features (Standard Deviation, Sum-Variance, Contrast) that describe the elastogram's contrast, variability and complexity. Sensitivity and specificity values were 93.3% and 94.0% respectively.

Conclusion:

The proposed classification scheme can provide reproducibility and reliability of liver SWE application in clinical practice. It can also assist the interpretation of SWE measurements which is considered as a difficult task due to absence of guidelines in the literature and to decrease the time/cost of diagnosis and the need for patients to undergo invasive examination.

This research has been co-financed by the European Union (European Social Fund ESF) and Greek national funds through the Operational Program ’Education and Lifelong Learning’ of the National Strategic Reference Framework (NSRF) Research Funding Program: ARCHIMEDES III. Investing in knowledge society through the European Social Fund.

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