ArticlePloS one2015
Hepatitis C related chronic liver cirrhosis: feasibility of texture analysis of MR images for classification of fibrosis stage and necroinflammatory activity grade.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 46 citations in OpenAlex.
- Predicting post-hepatectomy liver failure with T1 mapping-based whole-liver histogram analysis on gadoxetic acid-enhanced MRI: comparison with the indocyanine green clearance test and albumin-bilirubin scoring system.European radiology · 2025Article
- Comparative Study of Cell Nuclei Segmentation Based on Computational and Handcrafted Features Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2025Article
- Vasomics of the liver.Gut · 2025Review
- Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine.Scientific reports · 2025Article
- Computed tomography-based multi-organ radiomics nomogram model for predicting the risk of esophagogastric variceal bleeding in cirrhosis.World journal of gastroenterology · 2024Article
- Hybrid model for precise hepatitis-C classification using improved random forest and SVM method.Scientific reports · 2023Article
- Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis.Canadian journal of gastroenterology & hepatology · 2022Article
- Development of an equation to screen for solar hemorrhages from digital cushion ultrasound texture analysis in veal calves at slaughter.Frontiers in veterinary science · 2022Article
- The Value of First-Order Features Based on the Apparent Diffusion Coefficient Map in Evaluating the Therapeutic Effect of Low-Intensity Pulsed Ultrasound for Acute Traumatic Brain Injury With a Rat Model.Frontiers in computational neuroscience · 2022Article
- Application of Ultrasound Images Texture Analysis for the Estimation of Intramuscular Fat Content in theAnimals : an open access journal from MDPI · 2021Article
- Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI.Biomolecules · 2021Article
- Texture Analysis of Gray-Scale Ultrasound Images for Staging of Hepatic Fibrosis.Taehan Yongsang Uihakhoe chi · 2021Article
- Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment.Scientific reports · 2020Article
- Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis.Chinese medical journal · 2020Article
- Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting liver failure.World journal of gastroenterology · 2020Article
- Whole-Volume ADC Histogram Analysis in Parotid Glands to Identify Patients with Sjögren's Syndrome.Scientific reports · 2019Article
- Whole-volume ADC Histogram and Texture Analyses of Parotid Glands as an Image Biomarker in Evaluating Disease Activity of Primary Sjögren's Syndrome.Scientific reports · 2018Article
- Gestational and lactational exposure to dichlorinated bisphenol A induces early alterations of hepatic lipid composition in mice.Magma (New York, N.Y.) · 2018Article
- Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis.European radiology · 2018Article
- Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility.Journal of medical imaging (Bellingham, Wash.) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo assess the feasibility of texture analysis for classifying fibrosis stage and necroinflammatory activity grade in patients with chronic hepatitis C on T2-weighted (T2W), T1-weighted (T1W) and Gd-EOB-DTPA-enhanced hepatocyte-phase (EOB-HP) imaging. MATERIALS AND
methodsFrom April 2008 to June 2012, MR images from 123 patients with pathologically proven chronic hepatitis C were retrospectively analyzed. Texture parameters derived from histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model and wavelet transform methods were estimated with imaging software. Fisher, probability of classification error and average correlation, and mutual information coefficients were used to extract subsets of optimized texture features. Linear discriminant analysis in combination with 1-nearest neighbor classifier (LDA/1-NN) was used for lesion classification. In compliance with the software requirement, classification was performed based on datasets from all patients, the patient group with necroinflammatory activity grade 1, and that with fibrosis stage 4, respectively.
resultsBased on all patient dataset, LDA/1-NN produced misclassification rates of 28.46%, 35.77% and 20.33% for fibrosis staging and 34.15%, 25.20% and 28.46% for necroinflammatory activity grading in T2W, T1W and EOB-HP images. In the patient group with necroinflammatory activity grade 1, LDA/1-NN yielded misclassification rates of 5.00%, 0% and 12.50% for fibrosis staging in T2W, T1W and EOB-HP images respectively. In the patient group with fibrosis stage 4, LDA/1-NN yielded misclassification rates of 5.88%, 12.94% and 11.76% for necroinflammatory activity grading in T2W, T1W and EOB-HP images respectively.
conclusionTexture quantitative parameters of MR images facilitate classification of the fibrosis stage as well as necroinflammatory activity grade in chronic hepatitis C, especially after categorizing the input dataset according to the activity or fibrosis degree in order to remove the interference between the fibrosis stage and necroinflammatory activity grade on texture features.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.