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A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging.

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Title: A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging.
Authors: Gatos, Ilias1, Tsantis, Stavros1, Spiliopoulos, Stavros2, Karnabatidis, Dimitris3, Theotokas, Ioannis4, Zoumpoulis, Pavlos4, Loupas, Thanasis5, Hazle, John D.6, Kagadis, George C.7
Source: Medical Physics. Mar2016, Vol. 43 Issue 3, p1428-1436. 9p.
Subjects: Liver diseases, Prognosis, Elastography, Computed tomography, Medical radiology, Medical technology, Computer-assisted image analysis (Medicine)
Abstract: Purpose: Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system. Methods: The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system. Results: With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01±}0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77-0.89] confidence interval. Conclusions: The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging.
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  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Mar2016, Vol. 43 Issue 3, p1428-1436. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Liver+diseases%22">Liver diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Prognosis%22">Prognosis</searchLink><br /><searchLink fieldCode="DE" term="%22Elastography%22">Elastography</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+radiology%22">Medical radiology</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+technology%22">Medical technology</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink>
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  Data: Purpose: Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system. Methods: The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system. Results: With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01±}0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77-0.89] confidence interval. Conclusions: The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1118/1.4942383
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      – SubjectFull: Liver diseases
        Type: general
      – SubjectFull: Prognosis
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      – SubjectFull: Elastography
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      – SubjectFull: Computed tomography
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      – SubjectFull: Medical radiology
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      – SubjectFull: Medical technology
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      – SubjectFull: Computer-assisted image analysis (Medicine)
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              Text: Mar2016
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