A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features

Detalhes bibliográficos
Autor(a) principal: Girardi,Fábio Muradás
Data de Publicação: 2019
Outros Autores: Silva,Laura Mezzomo da, Flores,Cecilia Dias
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Journal of Otorhinolaryngology
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1808-86942019000100024
Resumo: Abstract Introduction: A discussion in literature about a standardized decision support tool for the management of thyroid nodules remains. Objective: The purpose of this study was to create a statistical prediction model for thyroid nodules management. Methods: Two hundred and four benign and 57 malignant thyroid nodules were selected for a retrospective study. The variables age, gender and ultrasonographic features were examined using univariate and multivariate models. A statistical formula was used to calculate the risk of cancer of each case. Results: In multivariate analysis, irregular shape, absence of halo, lower mean age, homogeneous echotexture, microcalcifications and solid content were associated with cancer. After applying the formula, 20 cases (7.6%) with a calculated risk for malignancy ≤3.0% were found, all of them benign. Setting the calculated risk in ≥80%, 21 (8.0%) cases were selected, and in 85.7% of them cancer was confirmed in histopathology. Internal accuracy of the prediction formula was 92.5%. Conclusions: The prediction formula reached high accuracy and may be an alternative to other decision support tools for thyroid nodule management.
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spelling A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic featuresThyroid noduleThyroid neoplasmsUltrasonographyCytologyBiopsy needleAbstract Introduction: A discussion in literature about a standardized decision support tool for the management of thyroid nodules remains. Objective: The purpose of this study was to create a statistical prediction model for thyroid nodules management. Methods: Two hundred and four benign and 57 malignant thyroid nodules were selected for a retrospective study. The variables age, gender and ultrasonographic features were examined using univariate and multivariate models. A statistical formula was used to calculate the risk of cancer of each case. Results: In multivariate analysis, irregular shape, absence of halo, lower mean age, homogeneous echotexture, microcalcifications and solid content were associated with cancer. After applying the formula, 20 cases (7.6%) with a calculated risk for malignancy ≤3.0% were found, all of them benign. Setting the calculated risk in ≥80%, 21 (8.0%) cases were selected, and in 85.7% of them cancer was confirmed in histopathology. Internal accuracy of the prediction formula was 92.5%. Conclusions: The prediction formula reached high accuracy and may be an alternative to other decision support tools for thyroid nodule management.Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial.2019-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1808-86942019000100024Brazilian Journal of Otorhinolaryngology v.85 n.1 2019reponame:Brazilian Journal of Otorhinolaryngologyinstname:Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial (ABORL-CCF)instacron:ABORL-CCF10.1016/j.bjorl.2017.10.001info:eu-repo/semantics/openAccessGirardi,Fábio MuradásSilva,Laura Mezzomo daFlores,Cecilia Diaseng2019-02-05T00:00:00Zoai:scielo:S1808-86942019000100024Revistahttp://www.bjorl.org.br/https://old.scielo.br/oai/scielo-oai.phprevista@aborlccf.org.br||revista@aborlccf.org.br1808-86861808-8686opendoar:2019-02-05T00:00Brazilian Journal of Otorhinolaryngology - Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial (ABORL-CCF)false
dc.title.none.fl_str_mv A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features
title A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features
spellingShingle A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features
Girardi,Fábio Muradás
Thyroid nodule
Thyroid neoplasms
Ultrasonography
Cytology
Biopsy needle
title_short A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features
title_full A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features
title_fullStr A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features
title_full_unstemmed A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features
title_sort A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features
author Girardi,Fábio Muradás
author_facet Girardi,Fábio Muradás
Silva,Laura Mezzomo da
Flores,Cecilia Dias
author_role author
author2 Silva,Laura Mezzomo da
Flores,Cecilia Dias
author2_role author
author
dc.contributor.author.fl_str_mv Girardi,Fábio Muradás
Silva,Laura Mezzomo da
Flores,Cecilia Dias
dc.subject.por.fl_str_mv Thyroid nodule
Thyroid neoplasms
Ultrasonography
Cytology
Biopsy needle
topic Thyroid nodule
Thyroid neoplasms
Ultrasonography
Cytology
Biopsy needle
description Abstract Introduction: A discussion in literature about a standardized decision support tool for the management of thyroid nodules remains. Objective: The purpose of this study was to create a statistical prediction model for thyroid nodules management. Methods: Two hundred and four benign and 57 malignant thyroid nodules were selected for a retrospective study. The variables age, gender and ultrasonographic features were examined using univariate and multivariate models. A statistical formula was used to calculate the risk of cancer of each case. Results: In multivariate analysis, irregular shape, absence of halo, lower mean age, homogeneous echotexture, microcalcifications and solid content were associated with cancer. After applying the formula, 20 cases (7.6%) with a calculated risk for malignancy ≤3.0% were found, all of them benign. Setting the calculated risk in ≥80%, 21 (8.0%) cases were selected, and in 85.7% of them cancer was confirmed in histopathology. Internal accuracy of the prediction formula was 92.5%. Conclusions: The prediction formula reached high accuracy and may be an alternative to other decision support tools for thyroid nodule management.
publishDate 2019
dc.date.none.fl_str_mv 2019-02-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1808-86942019000100024
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1808-86942019000100024
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1016/j.bjorl.2017.10.001
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial.
publisher.none.fl_str_mv Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial.
dc.source.none.fl_str_mv Brazilian Journal of Otorhinolaryngology v.85 n.1 2019
reponame:Brazilian Journal of Otorhinolaryngology
instname:Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial (ABORL-CCF)
instacron:ABORL-CCF
instname_str Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial (ABORL-CCF)
instacron_str ABORL-CCF
institution ABORL-CCF
reponame_str Brazilian Journal of Otorhinolaryngology
collection Brazilian Journal of Otorhinolaryngology
repository.name.fl_str_mv Brazilian Journal of Otorhinolaryngology - Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial (ABORL-CCF)
repository.mail.fl_str_mv revista@aborlccf.org.br||revista@aborlccf.org.br
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