Performance of radiomic models in the prediction of lymph node metastasis in papillary thyroid cancer patients

Detalhes bibliográficos
Autor(a) principal: Oliveira, Rafael Silva Lopes
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/39365
Resumo: This research presents a meta-analysis reviewing the use of Radiomics in oncology, specifically in papillary thyroid cancer (PTC) patients. The study investigates the potential of radiomic features for diagnosing lymph node metastasis in PTC and the impact of imaging modalities on machine learning models. Sub-group analysis reveals significant differences in specificity among imaging modalities and lymph node categories, while sensitivity remains unchanged. Ultrasonography shows higher specificity but limitations in central cervical lymph nodes. The findings highlight the heterogeneity of diagnostic accuracy within different anatomical regions. Bivariate meta-regression analysis confirms sub-group results and combining sub-group analysis and meta-regression aids clinical decision-making, considering tumor heterogeneity.
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spelling Performance of radiomic models in the prediction of lymph node metastasis in papillary thyroid cancer patientsRadiomicsPapillary thyroid cancerMachine learningMeta-analysisMedical imagingImaging biomarkersThis research presents a meta-analysis reviewing the use of Radiomics in oncology, specifically in papillary thyroid cancer (PTC) patients. The study investigates the potential of radiomic features for diagnosing lymph node metastasis in PTC and the impact of imaging modalities on machine learning models. Sub-group analysis reveals significant differences in specificity among imaging modalities and lymph node categories, while sensitivity remains unchanged. Ultrasonography shows higher specificity but limitations in central cervical lymph nodes. The findings highlight the heterogeneity of diagnostic accuracy within different anatomical regions. Bivariate meta-regression analysis confirms sub-group results and combining sub-group analysis and meta-regression aids clinical decision-making, considering tumor heterogeneity.Este trabalho apresenta uma revisão e meta-análise que sumariza o uso da Radiómica em oncologia, especificamente em pacientes com cancro da tiróide papilar. Este projeto investiga o potencial do uso de características radiómicas para o diagnóstico de metástase em nódulos linfáticos em pacientes com cancro papilar da tiróide e o impacto das modalidades de imagem em modelos de aprendizagem computacional. A análise de subgrupos revela diferenças significativas na especificidade entre as modalidades de imagem e as categorias de nódulos linfáticos, enquanto a sensibilidade permanece inalterada. Ultrassom mostra uma especificidade mais alta, mas com limitações nos nódulos linfáticos cervicais centrais. Os resultados destacam a heterogeneidade na precisão diagnóstica dentro de diferentes regiões anatómicas. A análise de meta-regressão bivariada confirma os resultados dos subgrupos e a combinação da análise de subgrupos e meta-regressão auxilia na tomada de decisões clínicas, considerando a hetergeneidade tumoral.2023-09-12T09:34:11Z2023-07-24T00:00:00Z2023-07-24info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/39365engOliveira, Rafael Silva Lopesinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T12:16:53Zoai:ria.ua.pt:10773/39365Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:09:33.819529Repositório 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 Performance of radiomic models in the prediction of lymph node metastasis in papillary thyroid cancer patients
title Performance of radiomic models in the prediction of lymph node metastasis in papillary thyroid cancer patients
spellingShingle Performance of radiomic models in the prediction of lymph node metastasis in papillary thyroid cancer patients
Oliveira, Rafael Silva Lopes
Radiomics
Papillary thyroid cancer
Machine learning
Meta-analysis
Medical imaging
Imaging biomarkers
title_short Performance of radiomic models in the prediction of lymph node metastasis in papillary thyroid cancer patients
title_full Performance of radiomic models in the prediction of lymph node metastasis in papillary thyroid cancer patients
title_fullStr Performance of radiomic models in the prediction of lymph node metastasis in papillary thyroid cancer patients
title_full_unstemmed Performance of radiomic models in the prediction of lymph node metastasis in papillary thyroid cancer patients
title_sort Performance of radiomic models in the prediction of lymph node metastasis in papillary thyroid cancer patients
author Oliveira, Rafael Silva Lopes
author_facet Oliveira, Rafael Silva Lopes
author_role author
dc.contributor.author.fl_str_mv Oliveira, Rafael Silva Lopes
dc.subject.por.fl_str_mv Radiomics
Papillary thyroid cancer
Machine learning
Meta-analysis
Medical imaging
Imaging biomarkers
topic Radiomics
Papillary thyroid cancer
Machine learning
Meta-analysis
Medical imaging
Imaging biomarkers
description This research presents a meta-analysis reviewing the use of Radiomics in oncology, specifically in papillary thyroid cancer (PTC) patients. The study investigates the potential of radiomic features for diagnosing lymph node metastasis in PTC and the impact of imaging modalities on machine learning models. Sub-group analysis reveals significant differences in specificity among imaging modalities and lymph node categories, while sensitivity remains unchanged. Ultrasonography shows higher specificity but limitations in central cervical lymph nodes. The findings highlight the heterogeneity of diagnostic accuracy within different anatomical regions. Bivariate meta-regression analysis confirms sub-group results and combining sub-group analysis and meta-regression aids clinical decision-making, considering tumor heterogeneity.
publishDate 2023
dc.date.none.fl_str_mv 2023-09-12T09:34:11Z
2023-07-24T00:00:00Z
2023-07-24
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/39365
url http://hdl.handle.net/10773/39365
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv 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
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