SAT-474-Misdiagnosis of focal liver lesions by means of CEUS

Moga, Tudor; Şirli, Roxana; Popescu, A; Dănilă, Mirela; Ghiuchici, Ana-Maria; Sporea, Ioan

doi:10.1016/s0618-8278(19)31681-0

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“…However, there are situations in which CEUS cannot be confidently relied upon when evaluating an FLL. Factors such as liver cirrhosis, the dimension of the lesion, histological differentiation, vascular abnormalities, a lack of an acoustic window, or even focal fatty sparing might lead to misdiagnosis during CEUS [1,3,16,17].…”

Section: Discussionmentioning

confidence: 99%

“…By providing contrast for US, broad spectrums of features have been implemented in US machines, allowing us to quantitatively analyze organ perfusion in terms of time and intensity (time-intensity curve and parametric imaging analysis). However, CEUS examination for FLL might have some drawbacks related to the echoic window, liver cirrhosis, and the examiner's experience [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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Multiparametric Ultrasound Approach Using a Tree-Based Decision Classifier for Inconclusive Focal Liver Lesions Evaluated by Contrast Enhanced Ultrasound

Moga

David

Popescu

et al. 2021

JPM

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Background: Multiparametric 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 methods: We 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. Results: Area 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%). Conclusions: By 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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%