Trial reportHepatology international2022
Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker.
Trial report in Hepatology international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
What it found
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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
33 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Physics-aware imaging AI for quantitative MASLD biomarker mapping: a systematic review of deep learning and radiomics across ultrasound, CT, and MRI.Abdominal radiology (New York) · 2026Pooled it
- Role of radiomics in staging liver fibrosis: a meta-analysis.BMC medical imaging · 2024Pooled it
- Radiomics based on enhanced CT for differentiating between pulmonary tuberculosis and pulmonary adenocarcinoma presenting as solid nodules or masses.Journal of cancer research and clinical oncology · 2023Trial
- Differentiating advanced from non-advanced hepatic fibrosis: a hybrid deep learning-radiomics model leveraging synthetic contrast-enhanced CT from CycleGAN.Abdominal radiology (New York) · 2026Article
- Lauren classification-based combined model integrating clinicopathological and spectral CT features for disease-free survival prediction in gastric cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Research Progress on the Application of Radiomics and Deep Learning in Liver Fibrosis.Journal of imaging · 2026Review
- Multi-Phasic CECT Peritumoral Radiomics Predict Treatment Response to Bevacizumab-Based Chemotherapy in RAS-Mutated Colorectal Liver Metastases.Bioengineering (Basel, Switzerland) · 2026Article
- Radiomics-based diagnosis of acute cholangitis using non-contrast computed tomography: a preliminary study.Radiologie (Heidelberg, Germany) · 2026Article
- Integration of dual-energy computed tomography and radiomics to improve noninvasive assessment of liver fibrosis: A retrospective study.Journal of clinical imaging science · 2026Article
- A comprehensive narrative review of artificial intelligence use in the diagnosis and management of metabolic dysfunction-associated steatotic liver disease.Translational gastroenterology and hepatology · 2026Review
- Radiomics-based diagnosis of carotid artery stenosis using non-contrast CT: model development and validation.European journal of medical research · 2025Article
- CT-Based Radiomic Models in Biopsy-Proven Liver Fibrosis Staging: Direct Comparison of Segmentation Types and Organ Inclusion.Diagnostics (Basel, Switzerland) · 2025Article
- Identification of abdominal MRI features associated with histopathological severity and treatment response in autoimmune hepatitis.European radiology · 2025Article
- Non-invasive liver fibrosis screening on CT images using radiomics.BMC medical imaging · 2025Article
- Predictive value of high-risk esophageal varices in cirrhosis based on dual-energy CT combined with clinical and serologic features.BMC medical imaging · 2025Article
- Three-dimensional organ segmentation-derived CT attenuation parameters for assessing hepatic steatosis in chronic hepatitis B patients.Scientific reports · 2025Article
- Noninvasive Grading of Liver Fibrosis Based on Texture Analysis From MRI-Derived Radiomics.NMR in biomedicine · 2025Article
- Intratumoral and peritumoral radiomics using multi-phase contrast-enhanced CT for diagnosis of renal oncocytoma and chromophobe renal cell carcinoma: a multicenter retrospective study.Frontiers in oncology · 2025Article
- A combination model of CT-based radiomics and clinical biomarkers for staging liver fibrosis in the patients with chronic liver disease.Scientific reports · 2024Article
- Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis.Journal of translational medicine · 2024Review
Corrections and comments
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Authors and funding
9 authors at 1 institution in 2 countries.
Funding
Abstract
backgroundTo establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images. MATERIALS AND
methodsThis retrospective study developed two radiomics-based models (R-score: radiomics signature; R-fibrosis: integrate radiomic and serum variables) in a training cohort of 332 patients (median age, 59 years; interquartile range, 51-67 years; 256 men) with biopsy-proven liver fibrosis who underwent contrast-enhanced CT between January 2017 and December 2020. Radiomic features were extracted from non-contrast, arterial and portal phase CT images and selected using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3-F4 from stage F0-F2. Optimal cutoffs to diagnose significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4) and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis. An internal validation was conducted in 111 randomly assigned patients (median age, 58 years; interquartile range, 49-66 years; 89 men).
resultsIn the validation cohort, R-score and R-fibrosis (Obuchowski index, 0.843 and 0.846, respectively) significantly outperformed aspartate transaminase-to-platelet ratio (APRI) (Obuchowski index, 0.651; p < .001) and fibrosis-4 index (FIB-4) (Obuchowski index, 0.676; p < .001) for staging liver fibrosis. Using the cutoffs, R-fibrosis and R-score had a sensitivity range of 70-87%, specificity range of 71-97%, and accuracy range of 82-86% in diagnosing significant fibrosis, advanced fibrosis and cirrhosis.
conclusionRadiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis.
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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.