ArticleCancers2023
Utilization of a Machine Learning Algorithm for the Application of Ancillary Features to LI-RADS Categories LR3 and LR4 on Gadoxetate Disodium-Enhanced MRI.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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Who cites it
2 citing papers in PubMed.
- Diagnostic performance of liver imaging reporting and data system ancillary features for hepatocellular carcinoma: a systematic review and evidence synthesis.Abdominal radiology (New York) · 2026Review
- Diagnosis and management of indeterminate liver nodules in patients with cirrhosis.Clinical liver disease · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundThis study aimed to identify the important ancillary features (AFs) and determine the utilization of a machine-learning-based strategy for applying AFs for LI-RADS LR3/4 observations on gadoxetate disodium-enhanced MRI.
methodsWe retrospectively analyzed MRI features of LR3/4 determined with only major features. Uni- and multivariate analyses and random forest analysis were performed to identify AFs associated with HCC. A decision tree algorithm of applying AFs for LR3/4 was compared with other alternative strategies using McNemar's test.
resultsWe evaluated 246 observations from 165 patients. In multivariate analysis, restricted diffusion and mild-moderate T2 hyperintensity showed independent associations with HCC (odds ratios: 12.4 [
conclusionOur decision tree algorithm of applying AFs for LR3/4 shows significantly increased AUC, sensitivity, and accuracy but reduced specificity. These appear to be more appropriate in certain circumstances in which there is an emphasis on the early detection of HCC.
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