ArticleJournal of personalized medicine2021
Multiparametric Ultrasound Approach Using a Tree-Based Decision Classifier for Inconclusive Focal Liver Lesions Evaluated by Contrast Enhanced Ultrasound.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Challenges in Diagnosing Focal Liver Lesions Using Contrast-Enhanced Ultrasound.Diagnostics (Basel, Switzerland) · 2024Article
- A Retrospective Analysis of SystemicMicroorganisms · 2024Article
- Phenotype of Idiopathic Infantile Hypercalcemia Associated with the Heterozygous Pathogenic Variant ofChildren (Basel, Switzerland) · 2023Article
- Current aspects of multimodal ultrasound liver diagnostics using contrast-enhanced ultrasonography (CEUS), fat evaluation, fibrosis assessment, and perfusion analysis - An update.Clinical hemorheology and microcirculation · 2023Article
- Contrast-Enhanced Ultrasonography with Arrival Time Parametric Imaging as a Non-Invasive Diagnostic Tool for Liver Cirrhosis.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
13 authors.
Funding
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Abstract
backgroundMultiparametric ultrasound (MPUS) is a concept whereby the examiner is encouraged to use the latest features of an ultrasound machine. The aim of this study was to reanalyze inconclusive focal liver lesions (FLLs) that had been analyzed via contrast enhanced ultrasound (CEUS) using the MPUS approach with the help of a tree-based decision classifier. MATERIALS AND
methodsWe retrospectively analyzed FLLs that were inconclusive upon CEUS examination in our department, focusing our attention on samples taken over a period of two years (2017-2018). MPUS reanalysis followed a three-step algorithm, taking into account the liver stiffness measurement (LSM), time-intensity curve analysis (TIC), and parametric imaging (PI). After processing all steps of the algorithm, a binary decision tree classifier (BDTC) was used to achieve a software-assisted decision.
resultsArea was the only TIC-CEUS parameter that showed a significant difference between malign and benign lesions with a cutoff of >-19.3 dB for washout phenomena (AUROC = 0.58, Se = 74.0%, Sp = 45.7%). Using the binary decision tree classifier (BDTC) algorithm, we correctly classified 71 out of 91 lesions according to their malignant or benignant status, with an accuracy of 78.0% (sensitivity = 62%, specificity = 45%, and precision = 80%).
conclusionsBy reevaluating inconclusive FLLs that had been analyzed via CEUS using MPUS, we managed to determine that 78% of the lesions were malignant and, in 28% of them, we established the lesion type.
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