Texture-based classification of liver fibrosis using MRI

Mike House, S.J. Bangma, M. Thomas, Eng Gan, Oyekoya T. Ayonrinde, Leon Adams, John Olynyk, Tim St. Pierre

    Research output: Contribution to journalArticle

    22 Citations (Scopus)

    Abstract

    © 2013 Wiley Periodicals, Inc. Purpose: To investigate the ability of texture analysis of MRI images to stage liver fibrosis. Current noninvasive approaches for detecting liver fibrosis have limitations and cannot yet routinely replace biopsy for diagnosing significant fibrosis. Materials and Methods: Forty-nine patients with a range of liver diseases and biopsy-confirmed fibrosis were enrolled in the study. For texture analysis all patients were scanned with a T2-weighted, high-resolution, spin echo sequence and Haralick texture features applied. The area under the receiver operating characteristics curve (AUROC) was used to assess the diagnostic performance of the texture analysis. Results: The best mean AUROC achieved for separating mild from severe fibrosis was 0.81. The inclusion of age, liver fat and liver R2 variables into the generalized linear model improved AUROC values for all comparisons, with the F0 versus F1-4 comparison the highest (0.91). Conclusion: Our results suggest that a combination of MRI measures, that include selected texture features from T2-weighted images, may be a useful tool for excluding fibrosis in patients with liver disease. However, texture analysis of MRI performs only modestly when applied to the classification of patients in the mild and intermediate fibrosis stages.
    Original languageEnglish
    Pages (from-to)322-328
    Number of pages7
    JournalJournal of Magnetic Resonance Imaging
    Volume41
    Issue number2
    DOIs
    Publication statusPublished - 22 Jan 2015

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    Liver Cirrhosis
    Fibrosis
    ROC Curve
    Liver Diseases
    Biopsy
    Liver
    Linear Models
    Fats

    Cite this

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    title = "Texture-based classification of liver fibrosis using MRI",
    abstract = "{\circledC} 2013 Wiley Periodicals, Inc. Purpose: To investigate the ability of texture analysis of MRI images to stage liver fibrosis. Current noninvasive approaches for detecting liver fibrosis have limitations and cannot yet routinely replace biopsy for diagnosing significant fibrosis. Materials and Methods: Forty-nine patients with a range of liver diseases and biopsy-confirmed fibrosis were enrolled in the study. For texture analysis all patients were scanned with a T2-weighted, high-resolution, spin echo sequence and Haralick texture features applied. The area under the receiver operating characteristics curve (AUROC) was used to assess the diagnostic performance of the texture analysis. Results: The best mean AUROC achieved for separating mild from severe fibrosis was 0.81. The inclusion of age, liver fat and liver R2 variables into the generalized linear model improved AUROC values for all comparisons, with the F0 versus F1-4 comparison the highest (0.91). Conclusion: Our results suggest that a combination of MRI measures, that include selected texture features from T2-weighted images, may be a useful tool for excluding fibrosis in patients with liver disease. However, texture analysis of MRI performs only modestly when applied to the classification of patients in the mild and intermediate fibrosis stages.",
    author = "Mike House and S.J. Bangma and M. Thomas and Eng Gan and Ayonrinde, {Oyekoya T.} and Leon Adams and John Olynyk and {St. Pierre}, Tim",
    year = "2015",
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    doi = "10.1002/jmri.24536",
    language = "English",
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    Texture-based classification of liver fibrosis using MRI. / House, Mike; Bangma, S.J.; Thomas, M.; Gan, Eng; Ayonrinde, Oyekoya T.; Adams, Leon; Olynyk, John; St. Pierre, Tim.

    In: Journal of Magnetic Resonance Imaging, Vol. 41, No. 2, 22.01.2015, p. 322-328.

    Research output: Contribution to journalArticle

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    AU - House, Mike

    AU - Bangma, S.J.

    AU - Thomas, M.

    AU - Gan, Eng

    AU - Ayonrinde, Oyekoya T.

    AU - Adams, Leon

    AU - Olynyk, John

    AU - St. Pierre, Tim

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    N2 - © 2013 Wiley Periodicals, Inc. Purpose: To investigate the ability of texture analysis of MRI images to stage liver fibrosis. Current noninvasive approaches for detecting liver fibrosis have limitations and cannot yet routinely replace biopsy for diagnosing significant fibrosis. Materials and Methods: Forty-nine patients with a range of liver diseases and biopsy-confirmed fibrosis were enrolled in the study. For texture analysis all patients were scanned with a T2-weighted, high-resolution, spin echo sequence and Haralick texture features applied. The area under the receiver operating characteristics curve (AUROC) was used to assess the diagnostic performance of the texture analysis. Results: The best mean AUROC achieved for separating mild from severe fibrosis was 0.81. The inclusion of age, liver fat and liver R2 variables into the generalized linear model improved AUROC values for all comparisons, with the F0 versus F1-4 comparison the highest (0.91). Conclusion: Our results suggest that a combination of MRI measures, that include selected texture features from T2-weighted images, may be a useful tool for excluding fibrosis in patients with liver disease. However, texture analysis of MRI performs only modestly when applied to the classification of patients in the mild and intermediate fibrosis stages.

    AB - © 2013 Wiley Periodicals, Inc. Purpose: To investigate the ability of texture analysis of MRI images to stage liver fibrosis. Current noninvasive approaches for detecting liver fibrosis have limitations and cannot yet routinely replace biopsy for diagnosing significant fibrosis. Materials and Methods: Forty-nine patients with a range of liver diseases and biopsy-confirmed fibrosis were enrolled in the study. For texture analysis all patients were scanned with a T2-weighted, high-resolution, spin echo sequence and Haralick texture features applied. The area under the receiver operating characteristics curve (AUROC) was used to assess the diagnostic performance of the texture analysis. Results: The best mean AUROC achieved for separating mild from severe fibrosis was 0.81. The inclusion of age, liver fat and liver R2 variables into the generalized linear model improved AUROC values for all comparisons, with the F0 versus F1-4 comparison the highest (0.91). Conclusion: Our results suggest that a combination of MRI measures, that include selected texture features from T2-weighted images, may be a useful tool for excluding fibrosis in patients with liver disease. However, texture analysis of MRI performs only modestly when applied to the classification of patients in the mild and intermediate fibrosis stages.

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