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08.04.2020 | Imaging Informatics and Artificial Intelligence | Ausgabe 8/2020

European Radiology 8/2020

Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology

Zeitschrift:
European Radiology > Ausgabe 8/2020
Autoren:
Khoschy Schawkat, Alexander Ciritsis, Sophie von Ulmenstein, Hanna Honcharova-Biletska, Christoph Jüngst, Achim Weber, Christoph Gubler, Joachim Mertens, Caecilia S. Reiner
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s00330-020-06831-8) contains supplementary material, which is available to authorized users.
Khoschy Schawkat and Alexander Ciritsis contributed equally to this article and share first authorship.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Objectives

To compare the diagnostic accuracy of texture analysis (TA)–derived parameters combined with machine learning (ML) of non-contrast-enhanced T1w and T2w fat-saturated (fs) images with MR elastography (MRE) for liver fibrosis quantification.

Methods

In this IRB-approved prospective study, liver MRIs of participants with suspected chronic liver disease who underwent liver biopsy between August 2015 and May 2018 were analyzed. Two readers blinded to clinical and histopathological findings performed TA. The participants were categorized into no or low-stage (0–2) and high-stage (3–4) fibrosis groups. Confusion matrices were calculated using a support vector machine combined with principal component analysis. The diagnostic accuracy of ML-based TA of liver fibrosis and MRE was assessed by area under the receiver operating characteristic curves (AUC). Histopathology served as reference standard.

Results

A total of 62 consecutive participants (40 men; mean age ± standard deviation, 48 ± 13 years) were included. The accuracy of TA and ML on T1w was 85.7% (95% confidence interval [CI] 63.7–97.0) and 61.9% (95% CI 38.4–81.9) on T2w fs for classification of liver fibrosis into low-stage and high-stage fibrosis. The AUC for TA on T1w was similar to MRE (0.82 [95% CI 0.59–0.95] vs. 0.92 [95% CI 0.71–0.99], p = 0.41), while the AUC for T2w fs was significantly lower compared to MRE (0.57 [95% CI 0.34–0.78] vs. 0.92 [95% CI 0.71–0.99], p = 0.008).

Conclusion

Our results suggest that liver fibrosis can be quantified with TA-derived parameters of T1w when combined with a ML algorithm with similar accuracy compared to MRE.

Key Points

Liver fibrosis can be categorized into low-stage fibrosis (0–2) and high-stage fibrosis (3–4) using texture analysis–derived parameters of T1-weighted images with a machine learning approach.
For the differentiation of low-stage fibrosis and high-stage fibrosis, the diagnostic accuracy of texture analysis on T1-weighted images combined with a machine learning algorithm is similar compared to MR elastography.

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