Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear images
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/153810 |
Resumo: | Background: Thyroid nodules diagnosed as 'atypia of undetermined significance/ follicular lesion of undetermined significance' (AUS/FLUS) or 'follicular neoplasm/ suspected follicular neoplasm' (FN/SFN), according to Bethesda’s classification, represena challenge in clinical practice. Computerized analysis of nuclear images (CANI) could be a useful tool for these cases. Our aim was to evaluate the ability of CANI to correctly classify AUS/FLUS and FN/SFN thyroid nodules for malignancy. Methods: We studied 101 nodules cytologically classified as AUS/FLUS (n = 68) or FN/SFN (n = 33) from 97 thyroidectomy patients. Slides with cytological material were submitted for manual selection and analysis of the follicular cell nuclei for morphometric and texture parameters using ImageJ software. The histologically benign and malignant lesions were compared for such parameters which were then evaluated for the capacity to predict malignancy using the classification and regression trees gini model. The intraclass coefficient of correlation was used to evaluate method reproducibility. Results: In AUS/FLUS nodule analysis, the benign and malignant nodules differed for entropy (P < 0.05), while the FN/SFN nodules differed for fractal analysis, coefficient of variation (CV) of roughness, and CV-entropy (P < 0.05). Considering the AUS/FLUS and FN/SFN nodules separately, it correctly classified 90.0 and 100.0% malignant nodules, with a correct global classification of 94.1 and 97%, respectively. We observed that reproducibility was substantially or nearly complete (0.61–0.93) in 10 of the 12 nuclear parameters evaluated. Conclusion: CANI demonstrated a high capacity for correctly classifying AUS/FLUS and FN/SFN thyroid nodules for malignancy. This could be a useful method to help increase diagnostic accuracy in the indeterminate thyroid cytology. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear imagesCell nucleusCytologyDiagnosisPhotographyThyroid neoplasmsBackground: Thyroid nodules diagnosed as 'atypia of undetermined significance/ follicular lesion of undetermined significance' (AUS/FLUS) or 'follicular neoplasm/ suspected follicular neoplasm' (FN/SFN), according to Bethesda’s classification, represena challenge in clinical practice. Computerized analysis of nuclear images (CANI) could be a useful tool for these cases. Our aim was to evaluate the ability of CANI to correctly classify AUS/FLUS and FN/SFN thyroid nodules for malignancy. Methods: We studied 101 nodules cytologically classified as AUS/FLUS (n = 68) or FN/SFN (n = 33) from 97 thyroidectomy patients. Slides with cytological material were submitted for manual selection and analysis of the follicular cell nuclei for morphometric and texture parameters using ImageJ software. The histologically benign and malignant lesions were compared for such parameters which were then evaluated for the capacity to predict malignancy using the classification and regression trees gini model. The intraclass coefficient of correlation was used to evaluate method reproducibility. Results: In AUS/FLUS nodule analysis, the benign and malignant nodules differed for entropy (P < 0.05), while the FN/SFN nodules differed for fractal analysis, coefficient of variation (CV) of roughness, and CV-entropy (P < 0.05). Considering the AUS/FLUS and FN/SFN nodules separately, it correctly classified 90.0 and 100.0% malignant nodules, with a correct global classification of 94.1 and 97%, respectively. We observed that reproducibility was substantially or nearly complete (0.61–0.93) in 10 of the 12 nuclear parameters evaluated. Conclusion: CANI demonstrated a high capacity for correctly classifying AUS/FLUS and FN/SFN thyroid nodules for malignancy. This could be a useful method to help increase diagnostic accuracy in the indeterminate thyroid cytology.BioScientifica20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10216/153810eng2049-361410.1530/EC-20-0648Hayashi, CYJaune, DTAOliveira, CCCoelho, BPMiot, HAMarques, MEATagliarini, JVCastilho, ECSoares, CSPOliveira, FRKSoares, PMazeto, GMFSinfo: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:RCAAP2023-11-29T15:20:58Zoai:repositorio-aberto.up.pt:10216/153810Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:21:20.763538Repositório 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 |
Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear images |
title |
Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear images |
spellingShingle |
Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear images Hayashi, CY Cell nucleus Cytology Diagnosis Photography Thyroid neoplasms |
title_short |
Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear images |
title_full |
Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear images |
title_fullStr |
Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear images |
title_full_unstemmed |
Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear images |
title_sort |
Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear images |
author |
Hayashi, CY |
author_facet |
Hayashi, CY Jaune, DTA Oliveira, CC Coelho, BP Miot, HA Marques, MEA Tagliarini, JV Castilho, EC Soares, CSP Oliveira, FRK Soares, P Mazeto, GMFS |
author_role |
author |
author2 |
Jaune, DTA Oliveira, CC Coelho, BP Miot, HA Marques, MEA Tagliarini, JV Castilho, EC Soares, CSP Oliveira, FRK Soares, P Mazeto, GMFS |
author2_role |
author author author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Hayashi, CY Jaune, DTA Oliveira, CC Coelho, BP Miot, HA Marques, MEA Tagliarini, JV Castilho, EC Soares, CSP Oliveira, FRK Soares, P Mazeto, GMFS |
dc.subject.por.fl_str_mv |
Cell nucleus Cytology Diagnosis Photography Thyroid neoplasms |
topic |
Cell nucleus Cytology Diagnosis Photography Thyroid neoplasms |
description |
Background: Thyroid nodules diagnosed as 'atypia of undetermined significance/ follicular lesion of undetermined significance' (AUS/FLUS) or 'follicular neoplasm/ suspected follicular neoplasm' (FN/SFN), according to Bethesda’s classification, represena challenge in clinical practice. Computerized analysis of nuclear images (CANI) could be a useful tool for these cases. Our aim was to evaluate the ability of CANI to correctly classify AUS/FLUS and FN/SFN thyroid nodules for malignancy. Methods: We studied 101 nodules cytologically classified as AUS/FLUS (n = 68) or FN/SFN (n = 33) from 97 thyroidectomy patients. Slides with cytological material were submitted for manual selection and analysis of the follicular cell nuclei for morphometric and texture parameters using ImageJ software. The histologically benign and malignant lesions were compared for such parameters which were then evaluated for the capacity to predict malignancy using the classification and regression trees gini model. The intraclass coefficient of correlation was used to evaluate method reproducibility. Results: In AUS/FLUS nodule analysis, the benign and malignant nodules differed for entropy (P < 0.05), while the FN/SFN nodules differed for fractal analysis, coefficient of variation (CV) of roughness, and CV-entropy (P < 0.05). Considering the AUS/FLUS and FN/SFN nodules separately, it correctly classified 90.0 and 100.0% malignant nodules, with a correct global classification of 94.1 and 97%, respectively. We observed that reproducibility was substantially or nearly complete (0.61–0.93) in 10 of the 12 nuclear parameters evaluated. Conclusion: CANI demonstrated a high capacity for correctly classifying AUS/FLUS and FN/SFN thyroid nodules for malignancy. This could be a useful method to help increase diagnostic accuracy in the indeterminate thyroid cytology. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2021-01-01T00:00:00Z |
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://hdl.handle.net/10216/153810 |
url |
https://hdl.handle.net/10216/153810 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2049-3614 10.1530/EC-20-0648 |
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.publisher.none.fl_str_mv |
BioScientifica |
publisher.none.fl_str_mv |
BioScientifica |
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 |
repository.mail.fl_str_mv |
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1799136129794965505 |