ArticleThe British journal of radiology2023
Value of multimodal MRI radiomics and machine learning in predicting staging liver fibrosis and grading inflammatory activity.
Article in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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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
6 citing papers in PubMed.
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Imaging-based fibrosis assessment and risk stratification in MASLD.Frontiers in medicine · 2026Review
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
- Multiparametric MRI-based whole-liver radiomics for predicting early-stage liver fibrosis in rabbits.The British journal of radiology · 2024Article
- Staging liver fibrosis: comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease.Abdominal radiology (New York) · 2024Article
- Advances in application of novel magnetic resonance imaging technologies in liver disease diagnosis.World journal of gastroenterology · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the value of radiomics models created based on non-contrast enhanced METHODS AND MATERIALS: Data of 280 patients with pathologically confirmed HF and 48 healthy volunteers were included. The participants were divided into the training set and the test set at the proportion of 4:1 by the random seed method. We used the Pyradiomics software to extract radiomics features, and then use the least absolute shrinkage and selection operator to select the optimal subset. Finally, we used the stochastic gradient descent classifier to build the prediction models. DeLong test was used to compare the diagnostic performance of the models. Receiver operating characteristics was used to evaluate the prediction ability of the models.
resultsThe diagnostic efficiency of the models based on
conclusionThe radiomics models built on ADVANCES IN KNOWLEDGE: Based on conventional MR sequences that are readily available in the clinic, namely unenhanced
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Registered trials
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