ArticleEuropean radiology2020
Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis.BMC gastroenterology · 2021Pooled it
- Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study.European radiology · 2023Trial
- Imaging of MASLD: clinical standards and novel methods.Diabetologia · 2026Review
- MRI-Derived Evaluation of Liver Function and Its Extension to K-Edge Photon-Counting CT: Focus on Gadolinium-Based Functional Biliary Imaging.Diagnostics (Basel, Switzerland) · 2026Review
- Advanced liver fibrosis detection using a two-stage deep learning approach on standard T2-weighted MRI.Abdominal radiology (New York) · 2026Article
- Preliminary quantitative assessment of liver and spleen MR imaging and elastography in clinically significant portal hypertension.Scientific reports · 2026Article
- From mechanisms to management: Early detection and improved treatment of MASLD and its related hepatocellular carcinoma.Hepatology communications · 2025Review
- Diagnostic Applications of Artificial Intelligence in Liver Diseases.Journal of clinical medicine · 2025Review
- A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease.Heliyon · 2024Article
- Feasibility and performance of spin-echo EPI MR elastography at 3 Tesla for staging hepatic fibrosis in the presence of hepatic iron overload.Abdominal radiology (New York) · 2024Article
- Review
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study.Insights into imaging · 2023Article
- Noninvasive imaging biomarkers for liver fibrosis in nonalcoholic fatty liver disease: current and future.Clinical and molecular hepatology · 2023Review
- Value of multimodal MRI radiomics and machine learning in predicting staging liver fibrosis and grading inflammatory activity.The British journal of radiology · 2023Article
- Artificial Intelligence in NAFLD: Will Liver Biopsy Still Be Necessary in the Future?Healthcare (Basel, Switzerland) · 2022Review
- Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI-based techniques.Abdominal radiology (New York) · 2022Review
- A novel radiomics signature based on T2-weighted imaging accurately predicts hepatic inflammation in individuals with biopsy-proven nonalcoholic fatty liver disease: a derivation and independent validation study.Hepatobiliary surgery and nutrition · 2022Article
- Liver fibrosis quantification.Abdominal radiology (New York) · 2022Review
- Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis.Canadian journal of gastroenterology & hepatology · 2022Article
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9 authors.
Funding
Abstract
objectivesTo 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.
methodsIn 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.
resultsA 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).
conclusionOur 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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