ArticleJournal of magnetic resonance imaging : JMRI2012
Noninvasive classification of hepatic fibrosis based on texture parameters from double contrast-enhanced magnetic resonance images.
Article in Journal of magnetic resonance imaging : JMRI, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Diffusion-Weighted Imaging as a Quantitative Imaging Biomarker for Predicting Proliferation Rate in Hepatocellular Carcinoma: Developing a Radiomics Nomogram.Journal of computer assisted tomographyTrial
- Opportunistic Evaluation of Trabecular Bone Texture by MRI Reflects Bone Mineral Density and Microarchitecture.The Journal of clinical endocrinology and metabolism · 2023Article
- Value of multimodal MRI radiomics and machine learning in predicting staging liver fibrosis and grading inflammatory activity.The British journal of radiology · 2023Article
- Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI-based techniques.Abdominal radiology (New York) · 2022Review
- Value of Intra-Perinodular Textural Transition Features from MRI in Distinguishing Between Benign and Malignant Testicular Lesions.Cancer management and research · 2021Article
- CT Image-Based Texture Analysis to Predict Microvascular Invasion in Primary Hepatocellular Carcinoma.Journal of digital imaging · 2020Article
- Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis.Chinese medical journal · 2020Article
- Diffusion-weighted imaging and texture analysis: current role for diffuse liver disease.Abdominal radiology (New York) · 2020Review
- Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology.European radiology · 2020Article
- Liver Fibrosis Conventional and Molecular Imaging Diagnosis Update.Journal of liver · 2019Article
- Imaging of Hepatic Fibrosis.Current gastroenterology reports · 2018Review
- Liver Fibrosis Quantification by Magnetic Resonance Imaging.Topics in magnetic resonance imaging : TMRI · 2017Review
- Evaluation of hepatic fibrosis: a review from the society of abdominal radiology disease focus panel.Abdominal radiology (New York) · 2017Review
- A comprehensive literatures update of clinical researches of superparamagnetic resonance iron oxide nanoparticles for magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2017Review
- Non-invasive diagnosis of liver fibrosis and cirrhosis.World journal of gastroenterology · 2015Review
- Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T.BioMed research international · 2015Article
- [Image postprocessing part 2: algorithms and workflow].Der Radiologe · 2013Article
- Customized first and second order statistics based operators to support advanced texture analysis of MRI images.Computational and mathematical methods in medicine · 2013Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeTo demonstrate a proof of concept that quantitative texture feature analysis of double contrast-enhanced magnetic resonance imaging (MRI) can classify fibrosis noninvasively, using histology as a reference standard. MATERIALS AND
methodsA Health Insurance Portability and Accountability Act (HIPAA)-compliant Institutional Review Board (IRB)-approved retrospective study of 68 patients with diffuse liver disease was performed at a tertiary liver center. All patients underwent double contrast-enhanced MRI, with histopathology-based staging of fibrosis obtained within 12 months of imaging. The MaZda software program was used to compute 279 texture parameters for each image. A statistical regularization technique, generalized linear model (GLM)-path, was used to develop a model based on texture features for dichotomous classification of fibrosis category (F ≤2 vs. F ≥3) of the 68 patients, with histology as the reference standard. The model's performance was assessed and cross-validated. There was no additional validation performed on an independent cohort.
resultsCross-validated sensitivity, specificity, and total accuracy of the texture feature model in classifying fibrosis were 91.9%, 83.9%, and 88.2%, respectively.
conclusionThis study shows proof of concept that accurate, noninvasive classification of liver fibrosis is possible by applying quantitative texture analysis to double contrast-enhanced MRI. Further studies are needed in independent cohorts of subjects.
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