ReviewGastroenterology report2020
Radiomics based on artificial intelligence in liver diseases: where we are?
Review in Gastroenterology report, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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.
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Who cites it
28 citing papers in PubMed.
- Rethinking nutritional risk in cancer: muscle quality, myosteatosis, and prognosis.Hepatobiliary surgery and nutrition · 2026Article
- Exploring radiomics as a predictor of hypertrophy of future liver remnant after portal vein embolization in biliary malignancies: a retrospective cohort study.Abdominal radiology (New York) · 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
- Clinical Applications of Artificial Intelligence (AI) in Human Cancer: Is It Time to Update the Diagnostic and Predictive Models in Managing Hepatocellular Carcinoma (HCC)?Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning model for non-alcoholic steatohepatitis diagnosis based on ultrasound radiomics.BMC medical imaging · 2024Article
- Multiparametric ultrasound imaging for early-stage steatosis: Comparison with magnetic resonance imaging-based proton density fat fraction.Medical physics · 2024Article
- The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review.Przeglad gastroenterologiczny · 2024Review
- Article
- Application and prospects of AI-based radiomics in ultrasound diagnosis.Visual computing for industry, biomedicine, and art · 2023Review
- Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases.Journal of translational medicine · 2023Article
- Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver.World journal of gastroenterology · 2023Review
- Association of food intake with a risk of metabolic dysfunction-associated fatty liver disease: a cross-sectional study.Gastroenterology report · 2023Article
- Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.PLOS digital health · 2023Article
- Clinical-radiomic analysis for non-invasive prediction of liver steatosis on non-contrast CT: A pilot study.Frontiers in genetics · 2023Article
- Review
- Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen.Journal of imaging · 2022Article
- Hidden-mortality risk among patients deemed "low-risk" following high-risk operations.Hepatobiliary surgery and nutrition · 2022Article
- 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
- MRE-based NASH score for diagnosis of nonalcoholic steatohepatitis in patients with nonalcoholic fatty liver disease.Hepatology international · 2022Article
- Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery.Medicina (Kaunas, Lithuania) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics uses computers to extract a large amount of information from different types of images, form various quantifiable features, and select relevant features using artificial-intelligence algorithms to build models, in order to predict the outcomes of clinical problems (such as diagnosis, treatment, prognosis, etc.). The study of liver diseases by radiomics will contribute to early diagnosis and treatment of liver diseases and improve survival and cure rates of liver diseases. This field is currently in the ascendant and may have great development in the future. Therefore, we summarize the progress of current research in this article and then point out the related deficiencies and the direction of future research.
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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.