Cargando…

Radiofrequency ablation of liver tumors: quantitative assessment of tumor coverage through CT image processing

BACKGROUND: Radiofrequency ablation (RFA) is one of the most promising non-surgical treatments for hepatic tumors. The assessment of the therapeutic efficacy of RFA is usually obtained by visual comparison of pre- and post-treatment CT images, but no numerical quantification is performed. METHODS: I...

Descripción completa

Detalles Bibliográficos
Autores principales: Passera, Katia, Selvaggi, Sabrina, Scaramuzza, Davide, Garbagnati, Francesco, Vergnaghi, Daniele, Mainardi, Luca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626768/
https://www.ncbi.nlm.nih.gov/pubmed/23324557
http://dx.doi.org/10.1186/1471-2342-13-3
_version_ 1782266243151036416
author Passera, Katia
Selvaggi, Sabrina
Scaramuzza, Davide
Garbagnati, Francesco
Vergnaghi, Daniele
Mainardi, Luca
author_facet Passera, Katia
Selvaggi, Sabrina
Scaramuzza, Davide
Garbagnati, Francesco
Vergnaghi, Daniele
Mainardi, Luca
author_sort Passera, Katia
collection PubMed
description BACKGROUND: Radiofrequency ablation (RFA) is one of the most promising non-surgical treatments for hepatic tumors. The assessment of the therapeutic efficacy of RFA is usually obtained by visual comparison of pre- and post-treatment CT images, but no numerical quantification is performed. METHODS: In this work, a novel method aiming at providing a more objective tool for the evaluation of RFA coverage is described. Image registration and segmentation techniques were applied to enable the visualization of the tumor and the corresponding post-RFA necrosis in the same framework. In addition, a set of numerical indexes describing tumor/necrosis overlap and their mutual position were computed. RESULTS: After validation of segmentation step, the method was applied on a dataset composed by 10 tumors, suspected not to be completed treated. Numerical indexes showed that only two tumors were totally treated and the percentage of a residual tumor was in the range of 5.12%-35.92%. CONCLUSIONS: This work represents a first attempt to obtain a quantitative tool aimed to assess the accuracy of RFA treatment. The possibility to visualize the tumor and the correspondent post-RFA necrosis in the same framework and the definition of some synthetic numerical indexes could help clinicians in ameliorating RFA treatment.
format Online
Article
Text
id pubmed-3626768
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-36267682013-04-24 Radiofrequency ablation of liver tumors: quantitative assessment of tumor coverage through CT image processing Passera, Katia Selvaggi, Sabrina Scaramuzza, Davide Garbagnati, Francesco Vergnaghi, Daniele Mainardi, Luca BMC Med Imaging Technical Advance BACKGROUND: Radiofrequency ablation (RFA) is one of the most promising non-surgical treatments for hepatic tumors. The assessment of the therapeutic efficacy of RFA is usually obtained by visual comparison of pre- and post-treatment CT images, but no numerical quantification is performed. METHODS: In this work, a novel method aiming at providing a more objective tool for the evaluation of RFA coverage is described. Image registration and segmentation techniques were applied to enable the visualization of the tumor and the corresponding post-RFA necrosis in the same framework. In addition, a set of numerical indexes describing tumor/necrosis overlap and their mutual position were computed. RESULTS: After validation of segmentation step, the method was applied on a dataset composed by 10 tumors, suspected not to be completed treated. Numerical indexes showed that only two tumors were totally treated and the percentage of a residual tumor was in the range of 5.12%-35.92%. CONCLUSIONS: This work represents a first attempt to obtain a quantitative tool aimed to assess the accuracy of RFA treatment. The possibility to visualize the tumor and the correspondent post-RFA necrosis in the same framework and the definition of some synthetic numerical indexes could help clinicians in ameliorating RFA treatment. BioMed Central 2013-01-16 /pmc/articles/PMC3626768/ /pubmed/23324557 http://dx.doi.org/10.1186/1471-2342-13-3 Text en Copyright © 2013 Passera et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Advance
Passera, Katia
Selvaggi, Sabrina
Scaramuzza, Davide
Garbagnati, Francesco
Vergnaghi, Daniele
Mainardi, Luca
Radiofrequency ablation of liver tumors: quantitative assessment of tumor coverage through CT image processing
title Radiofrequency ablation of liver tumors: quantitative assessment of tumor coverage through CT image processing
title_full Radiofrequency ablation of liver tumors: quantitative assessment of tumor coverage through CT image processing
title_fullStr Radiofrequency ablation of liver tumors: quantitative assessment of tumor coverage through CT image processing
title_full_unstemmed Radiofrequency ablation of liver tumors: quantitative assessment of tumor coverage through CT image processing
title_short Radiofrequency ablation of liver tumors: quantitative assessment of tumor coverage through CT image processing
title_sort radiofrequency ablation of liver tumors: quantitative assessment of tumor coverage through ct image processing
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626768/
https://www.ncbi.nlm.nih.gov/pubmed/23324557
http://dx.doi.org/10.1186/1471-2342-13-3
work_keys_str_mv AT passerakatia radiofrequencyablationoflivertumorsquantitativeassessmentoftumorcoveragethroughctimageprocessing
AT selvaggisabrina radiofrequencyablationoflivertumorsquantitativeassessmentoftumorcoveragethroughctimageprocessing
AT scaramuzzadavide radiofrequencyablationoflivertumorsquantitativeassessmentoftumorcoveragethroughctimageprocessing
AT garbagnatifrancesco radiofrequencyablationoflivertumorsquantitativeassessmentoftumorcoveragethroughctimageprocessing
AT vergnaghidaniele radiofrequencyablationoflivertumorsquantitativeassessmentoftumorcoveragethroughctimageprocessing
AT mainardiluca radiofrequencyablationoflivertumorsquantitativeassessmentoftumorcoveragethroughctimageprocessing