MUGA processing: intra and interoperator variability impact using manual and automated methods
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
DOI: | 10.25758/set.2225 |
Texto Completo: | https://doi.org/10.25758/set.2225 |
Resumo: | Introduction – Multigated acquisition (MUGA) scan is mainly used for the assessment of left ventricular ejection fraction (LVEF) in patients who undergo cardiotoxic chemotherapy drugs. When applying automatic (A) or manual (M) processing methods, some biases in the quantitative metrics can be obtained. The aim of this study is to evaluate the influence of A and M methods, specifically, the inter and intraoperative variability in accordance with the professional experience. Methods – A retrospective study was performed with 14 MUGA exams available in ESTeSL’s Xeleris™ Functional Imaging Workstation v. 1.0628 database. Three operators (OP) with no professional experience and two with more than 10 years of experience, processed every study five times for each method, using the EF Analysis™ and the Peak Filling Rate™. To perform the multiple comparisons, the Repeated Measures ANOVA, Friedman, t-test, and Wilcoxon tests were used, considering α=0.05. Results – Four of the OP presented statistically significant differences between methods in one or more parameters; similar values between experienced OP and between the non-experienced were observed when the A method was applied, and higher discrepancies were present for all parameters obtained by the M mode; higher LVEF, peak filling rate, and peak empying rate values were observed for the M method. Conclusion – Variability was found when comparing M and A processing methods, as well as interoperator variability associated with their level of experience. Despite that, there was a trend of less variability between the two experienced OP and in the A method. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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MUGA processing: intra and interoperator variability impact using manual and automated methodsProcessamento de estudos de angiografia de radionuclídeos em equilíbrio: impacto da variabilidade intra e interoperador por métodos manuais e automáticosAngiografia de radionuclídeos em equilíbrioFunção cardíacaSegmentaçãoFração de ejeção do ventrículo esquerdoParâmetros diastólicosEquilibrium radionuclide angiographyCardiac functionSegmentationLeft ventricular ejection fractionDiastolic parametersIntroduction – Multigated acquisition (MUGA) scan is mainly used for the assessment of left ventricular ejection fraction (LVEF) in patients who undergo cardiotoxic chemotherapy drugs. When applying automatic (A) or manual (M) processing methods, some biases in the quantitative metrics can be obtained. The aim of this study is to evaluate the influence of A and M methods, specifically, the inter and intraoperative variability in accordance with the professional experience. Methods – A retrospective study was performed with 14 MUGA exams available in ESTeSL’s Xeleris™ Functional Imaging Workstation v. 1.0628 database. Three operators (OP) with no professional experience and two with more than 10 years of experience, processed every study five times for each method, using the EF Analysis™ and the Peak Filling Rate™. To perform the multiple comparisons, the Repeated Measures ANOVA, Friedman, t-test, and Wilcoxon tests were used, considering α=0.05. Results – Four of the OP presented statistically significant differences between methods in one or more parameters; similar values between experienced OP and between the non-experienced were observed when the A method was applied, and higher discrepancies were present for all parameters obtained by the M mode; higher LVEF, peak filling rate, and peak empying rate values were observed for the M method. Conclusion – Variability was found when comparing M and A processing methods, as well as interoperator variability associated with their level of experience. Despite that, there was a trend of less variability between the two experienced OP and in the A method.Introdução – A angiografia de radionuclídeos em equilíbrio (ARNe) é principalmente realizada para determinar a fração de ejeção do ventrículo esquerdo (FEVE) em doentes submetidos a quimioterápicos cardiotóxicos. Quando aplicados métodos de processamento automáticos (A) ou manuais (M) podem ser obtidas distorções métricas. Este estudo teve como objetivo aferir a influência dos métodos A e M e avaliar a variabilidade inter e intraoperador associada a diferentes experiências profissionais. Métodos – Estudo retrospetivo com 14 exames ARNe existentes na base de dados da estação de processamento Xeleris™ Functional Imaging Workstation v. 1.0628 da ESTeSL. Três operadores (OP) sem experiência profissional e dois com mais de dez anos de experiência processaram cada estudo cinco vezes por cada método, recorrendo ao EF Analysis™ e ao Peak Filling Rate™. As múltiplas comparações foram realizadas com os testes ANOVA de medidas repetidas, Friedman, teste-t e Wilcoxon, considerando α=0,05. Resultados – Quatro dos OP apresentaram diferenças estatisticamente significativas entre métodos para um ou mais parâmetros; foram obtidos valores semelhantes entre os OP experientes e entre os não experientes quando se aplicou o método A e observaram-se maiores discrepâncias para todos os parâmetros obtidos pelo método M; obtiveram-se valores superiores de FEVE, taxas de esvaziamento e preenchimento máximas com o método M. Conclusão – Verificou-se variabilidade dos resultados obtidos a partir da comparação dos métodos de processamento M e A, bem como variabilidade do interoperador associada ao seu nível de experiência profissional. Contudo os dois OP experientes apresentaram menor discrepância de valores entre si e para o método A.Escola Superior de Tecnologia da Saúde de Lisboa (Instituto Politécnico de Lisboa)2022-07-28info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.25758/set.2225oai:journals.ipl.pt:article/533Saúde e Tecnologia; No. 22 (2019): Novembro 2019; 22-27Saúde & Tecnologia; N.º 22 (2019): Novembro 2019; 22-271646-9704reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPporhttps://journals.ipl.pt/stecnologia/article/view/533https://doi.org/10.25758/set.2225https://journals.ipl.pt/stecnologia/article/view/533/459Direitos de Autor (c) 2022 Saúde & Tecnologiainfo:eu-repo/semantics/openAccessBelo, RitaCarvalhal, CristianaFigueiredo, Sérgiocarol, ElisabeteVieira, Lina2022-12-20T10:58:43Zoai:journals.ipl.pt:article/533Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:21:21.291745Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
MUGA processing: intra and interoperator variability impact using manual and automated methods Processamento de estudos de angiografia de radionuclídeos em equilíbrio: impacto da variabilidade intra e interoperador por métodos manuais e automáticos |
title |
MUGA processing: intra and interoperator variability impact using manual and automated methods |
spellingShingle |
MUGA processing: intra and interoperator variability impact using manual and automated methods MUGA processing: intra and interoperator variability impact using manual and automated methods Belo, Rita Angiografia de radionuclídeos em equilíbrio Função cardíaca Segmentação Fração de ejeção do ventrículo esquerdo Parâmetros diastólicos Equilibrium radionuclide angiography Cardiac function Segmentation Left ventricular ejection fraction Diastolic parameters Belo, Rita Angiografia de radionuclídeos em equilíbrio Função cardíaca Segmentação Fração de ejeção do ventrículo esquerdo Parâmetros diastólicos Equilibrium radionuclide angiography Cardiac function Segmentation Left ventricular ejection fraction Diastolic parameters |
title_short |
MUGA processing: intra and interoperator variability impact using manual and automated methods |
title_full |
MUGA processing: intra and interoperator variability impact using manual and automated methods |
title_fullStr |
MUGA processing: intra and interoperator variability impact using manual and automated methods MUGA processing: intra and interoperator variability impact using manual and automated methods |
title_full_unstemmed |
MUGA processing: intra and interoperator variability impact using manual and automated methods MUGA processing: intra and interoperator variability impact using manual and automated methods |
title_sort |
MUGA processing: intra and interoperator variability impact using manual and automated methods |
author |
Belo, Rita |
author_facet |
Belo, Rita Belo, Rita Carvalhal, Cristiana Figueiredo, Sérgio carol, Elisabete Vieira, Lina Carvalhal, Cristiana Figueiredo, Sérgio carol, Elisabete Vieira, Lina |
author_role |
author |
author2 |
Carvalhal, Cristiana Figueiredo, Sérgio carol, Elisabete Vieira, Lina |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Belo, Rita Carvalhal, Cristiana Figueiredo, Sérgio carol, Elisabete Vieira, Lina |
dc.subject.por.fl_str_mv |
Angiografia de radionuclídeos em equilíbrio Função cardíaca Segmentação Fração de ejeção do ventrículo esquerdo Parâmetros diastólicos Equilibrium radionuclide angiography Cardiac function Segmentation Left ventricular ejection fraction Diastolic parameters |
topic |
Angiografia de radionuclídeos em equilíbrio Função cardíaca Segmentação Fração de ejeção do ventrículo esquerdo Parâmetros diastólicos Equilibrium radionuclide angiography Cardiac function Segmentation Left ventricular ejection fraction Diastolic parameters |
description |
Introduction – Multigated acquisition (MUGA) scan is mainly used for the assessment of left ventricular ejection fraction (LVEF) in patients who undergo cardiotoxic chemotherapy drugs. When applying automatic (A) or manual (M) processing methods, some biases in the quantitative metrics can be obtained. The aim of this study is to evaluate the influence of A and M methods, specifically, the inter and intraoperative variability in accordance with the professional experience. Methods – A retrospective study was performed with 14 MUGA exams available in ESTeSL’s Xeleris™ Functional Imaging Workstation v. 1.0628 database. Three operators (OP) with no professional experience and two with more than 10 years of experience, processed every study five times for each method, using the EF Analysis™ and the Peak Filling Rate™. To perform the multiple comparisons, the Repeated Measures ANOVA, Friedman, t-test, and Wilcoxon tests were used, considering α=0.05. Results – Four of the OP presented statistically significant differences between methods in one or more parameters; similar values between experienced OP and between the non-experienced were observed when the A method was applied, and higher discrepancies were present for all parameters obtained by the M mode; higher LVEF, peak filling rate, and peak empying rate values were observed for the M method. Conclusion – Variability was found when comparing M and A processing methods, as well as interoperator variability associated with their level of experience. Despite that, there was a trend of less variability between the two experienced OP and in the A method. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-07-28 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.25758/set.2225 oai:journals.ipl.pt:article/533 |
url |
https://doi.org/10.25758/set.2225 |
identifier_str_mv |
oai:journals.ipl.pt:article/533 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://journals.ipl.pt/stecnologia/article/view/533 https://doi.org/10.25758/set.2225 https://journals.ipl.pt/stecnologia/article/view/533/459 |
dc.rights.driver.fl_str_mv |
Direitos de Autor (c) 2022 Saúde & Tecnologia info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Direitos de Autor (c) 2022 Saúde & Tecnologia |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Escola Superior de Tecnologia da Saúde de Lisboa (Instituto Politécnico de Lisboa) |
publisher.none.fl_str_mv |
Escola Superior de Tecnologia da Saúde de Lisboa (Instituto Politécnico de Lisboa) |
dc.source.none.fl_str_mv |
Saúde e Tecnologia; No. 22 (2019): Novembro 2019; 22-27 Saúde & Tecnologia; N.º 22 (2019): Novembro 2019; 22-27 1646-9704 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1822183294360354816 |
dc.identifier.doi.none.fl_str_mv |
10.25758/set.2225 |