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Multiparametric Ultrasound Approach Using a Tree-Based Decision Classifier for Inconclusive Focal Liver Lesions Evaluated by Contrast Enhanced Ultrasound
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 M...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709328/ https://www.ncbi.nlm.nih.gov/pubmed/34945860 http://dx.doi.org/10.3390/jpm11121388 |
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author | Moga, Tudor Voicu David, Ciprian Popescu, Alina Lupusoru, Raluca Heredea, Darius Ghiuchici, Ana M. Foncea, Camelia Burdan, Adrian Sirli, Roxana Danilă, Mirela Ratiu, Iulia Bizerea-Moga, Teofana Sporea, Ioan |
author_facet | Moga, Tudor Voicu David, Ciprian Popescu, Alina Lupusoru, Raluca Heredea, Darius Ghiuchici, Ana M. Foncea, Camelia Burdan, Adrian Sirli, Roxana Danilă, Mirela Ratiu, Iulia Bizerea-Moga, Teofana Sporea, Ioan |
author_sort | Moga, Tudor Voicu |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8709328 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87093282021-12-25 Multiparametric Ultrasound Approach Using a Tree-Based Decision Classifier for Inconclusive Focal Liver Lesions Evaluated by Contrast Enhanced Ultrasound Moga, Tudor Voicu David, Ciprian Popescu, Alina Lupusoru, Raluca Heredea, Darius Ghiuchici, Ana M. Foncea, Camelia Burdan, Adrian Sirli, Roxana Danilă, Mirela Ratiu, Iulia Bizerea-Moga, Teofana Sporea, Ioan J Pers Med Article 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. MDPI 2021-12-20 /pmc/articles/PMC8709328/ /pubmed/34945860 http://dx.doi.org/10.3390/jpm11121388 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Moga, Tudor Voicu David, Ciprian Popescu, Alina Lupusoru, Raluca Heredea, Darius Ghiuchici, Ana M. Foncea, Camelia Burdan, Adrian Sirli, Roxana Danilă, Mirela Ratiu, Iulia Bizerea-Moga, Teofana Sporea, Ioan Multiparametric Ultrasound Approach Using a Tree-Based Decision Classifier for Inconclusive Focal Liver Lesions Evaluated by Contrast Enhanced Ultrasound |
title | Multiparametric Ultrasound Approach Using a Tree-Based Decision Classifier for Inconclusive Focal Liver Lesions Evaluated by Contrast Enhanced Ultrasound |
title_full | Multiparametric Ultrasound Approach Using a Tree-Based Decision Classifier for Inconclusive Focal Liver Lesions Evaluated by Contrast Enhanced Ultrasound |
title_fullStr | Multiparametric Ultrasound Approach Using a Tree-Based Decision Classifier for Inconclusive Focal Liver Lesions Evaluated by Contrast Enhanced Ultrasound |
title_full_unstemmed | Multiparametric Ultrasound Approach Using a Tree-Based Decision Classifier for Inconclusive Focal Liver Lesions Evaluated by Contrast Enhanced Ultrasound |
title_short | Multiparametric Ultrasound Approach Using a Tree-Based Decision Classifier for Inconclusive Focal Liver Lesions Evaluated by Contrast Enhanced Ultrasound |
title_sort | multiparametric ultrasound approach using a tree-based decision classifier for inconclusive focal liver lesions evaluated by contrast enhanced ultrasound |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709328/ https://www.ncbi.nlm.nih.gov/pubmed/34945860 http://dx.doi.org/10.3390/jpm11121388 |
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