ArticleEuropean radiology2018
Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis.
Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI-Based Models for Risk Prediction in MASLD: A Systematic Review.Digestive diseases and sciences · 2026Pooled it
- Accuracy of Non-Invasive Imaging Techniques for the Diagnosis of MASH in Patients With MASLD: A Systematic Review.Liver international : official journal of the International Association for the Study of the Liver · 2025Pooled it
- Radiomics and anaesthetic planning: Quantitative imaging as a new frontier in preoperative risk assessment.World journal of radiology · 2026Review
- Radiomics-Based Assessment of Portal Hypertension Severity and Risk Stratification of Cirrhotic Patients Using Routine CT Scans.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Radiomics-based diagnosis of acute cholangitis using non-contrast computed tomography: a preliminary study.Radiologie (Heidelberg, Germany) · 2026Article
- CT-based radiomics-clinical model for risk assessment of parenteral nutrition-associated hepatic steatosis in chronic intestinal failure and its metabolomic interpretation.Frontiers in nutrition · 2026Article
- Computed Tomography Radiomics and Machine Learning for Prediction of Histology-Based Hepatic Steatosis Scores.Diagnostics (Basel, Switzerland) · 2025Article
- Systematic Review: AI Applications in Liver Imaging with a Focus on Segmentation and Detection.Life (Basel, Switzerland) · 2025Review
- Repeatability of computed tomography liver radiomic features in a nonhuman primate model of diet-induced steatosis.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases.Journal of translational medicine · 2023Article
- Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future.Clinical and molecular hepatology · 2023Review
- Clinical-radiomic analysis for non-invasive prediction of liver steatosis on non-contrast CT: A pilot study.Frontiers in genetics · 2023Article
- Radiomics Analysis of Magnetic Resonance Proton Density Fat Fraction for the Diagnosis of Hepatic Steatosis in Patients With Suspected Non-Alcoholic Fatty Liver Disease.Journal of Korean medical science · 2022Article
- Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review.Bioengineering (Basel, Switzerland) · 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
- Computed Tomography-Based Radiomic Analysis for Preoperatively Predicting the Macrovesicular Steatosis Grade in Cadaveric Donor Liver Transplantation.BioMed research international · 2022Article
- A New SteatoScore in the Evaluation of Non-Alcoholic Liver Disease in Oncologic Patients.Frontiers in oncology · 2022Article
- Recurrence of Early Hepatocellular Carcinoma after Surgery May Be Related to Intestinal Oxidative Stress and the Development of a Predictive Model.Oxidative medicine and cellular longevity · 2022Article
- Advances in liver US, CT, and MRI: moving toward the future.European radiology experimental · 2021Review
- Why did European Radiology reject my radiomic biomarker paper? How to correctly evaluate imaging biomarkers in a clinical setting.European radiology · 2021Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo determine if texture analysis of non-contrast-enhanced CT (NECT) images is able to predict nonalcoholic steatohepatitis (NASH).
methodsNECT images from 88 patients who underwent a liver biopsy for the diagnosis of suspected NASH were assessed and texture feature parameters were obtained without and with filtration. The patient population was divided into a predictive learning dataset and a validation dataset, and further divided into groups according to the prediction of liver fibrosis as assessed by hyaluronic acid levels. The reference standard was the histological result of a liver biopsy. A predictive model for NASH was developed using parameters derived from the learning dataset that demonstrated areas under the receiver operating characteristic curve (AUC) of >0.65. The resulting model was then applied to the validation dataset.
resultsIn patients without suspected fibrosis, the texture parameter mean without filter and skewness with a 2-mm filter were selected for the NASH prediction model. The AUC of the predictive model for the validation dataset was 0.94 and the accuracy was 94%. In patients with suspicion of fibrosis, the mean without filtration and kurtosis with a 4-mm filter were selected for the NASH prediction model. The AUC for the validation dataset was 0.60 and the accuracy was 42%.
conclusionsIn patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH. KEY POINTS: • In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH. • The mean without filtration and skewness with a 2-mm filter were modest predictors of NASH in patients without suspicion of liver fibrosis. • Hepatic fibrosis masks the characteristic texture features of NASH.
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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.