Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images

Detalhes bibliográficos
Autor(a) principal: VALLE, MATHEUS del
Data de Publicação: 2021
Outros Autores: SANTOS, MOISES O. dos, SANTOS, SOFIA N. dos, BERNARDES, EMERSON S., ZEZELL, DENISE M., SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE
Tipo de documento: Artigo de conferência
Título da fonte: Repositório Institucional do IPEN
Texto Completo: http://repositorio.ipen.br/handle/123456789/32334
Resumo: The breast cancer is the most incident cancer in women. Evaluation of hormone receptors expression plays an important role to outline treatment strategies. FTIR spectroscopy imaging may be employed as an additional technique, providing extra information to help physicians. In this work, estrogen and progesterone receptors expression were evaluated using tumors biopsies from human cell lines inoculated in mice. FTIR images were collect from histological sections, and six machine learning models were applied and assessed. Xtreme gradient boost and Linear Discriminant Analysis presented the best accuracies results, indicating to be potential models for breast cancer classification tasks.
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spelling 2021-11-12T10:59:23Z2021-11-12T10:59:23ZMay 31 - June 2, 2021http://repositorio.ipen.br/handle/123456789/3233410.1109/SBFOTONIOPC50774.2021.94619460000-0001-7404-96060000-0002-0029-7313The breast cancer is the most incident cancer in women. Evaluation of hormone receptors expression plays an important role to outline treatment strategies. FTIR spectroscopy imaging may be employed as an additional technique, providing extra information to help physicians. In this work, estrogen and progesterone receptors expression were evaluated using tumors biopsies from human cell lines inoculated in mice. FTIR images were collect from histological sections, and six machine learning models were applied and assessed. Xtreme gradient boost and Linear Discriminant Analysis presented the best accuracies results, indicating to be potential models for breast cancer classification tasks.Submitted by Pedro Silva Filho (pfsilva@ipen.br) on 2021-11-12T10:59:23Z No. of bitstreams: 1 28102.pdf: 316112 bytes, checksum: c79d2fcfb58a82462cbca62ddb656194 (MD5)Made available in DSpace on 2021-11-12T10:59:23Z (GMT). No. of bitstreams: 1 28102.pdf: 316112 bytes, checksum: c79d2fcfb58a82462cbca62ddb656194 (MD5)Funda????o de Amparo ?? Pesquisa do Estado de S??o Paulo (FAPESP)Coordena????o de Aperfei??oamento de Pessoal de N??vel Superior (CAPES)Conselho Nacional de Desenvolvimento Cient??fico e Tecnol??gico (CNPq)FAPESP: 05/51689-2; 17/50332-0CAPES: PROCAD 88881.068505/2014-01; 001CNPq: INCT-465763/2014-6; PQ-309902/2017-7; 142229/2019-9IEEEmammary glandsneoplasmsfourier transformationimagesmachine learninghormonesreceptorsEvaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR imagesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectSBFOTON IOPCIPiscataway, NJ, USAOnline1520984111446412099693600600600600600VALLE, MATHEUS delSANTOS, MOISES O. dosSANTOS, SOFIA N. dosBERNARDES, EMERSON S.ZEZELL, DENISE M.SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCEinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do IPENinstname:Instituto de Pesquisas Energéticas e Nucleares (IPEN)instacron:IPEN281022021ZEZELL, DENISE M.BERNARDES, EMERSON S.SANTOS, SOFIA N. dosSANTOS, MOISES O. dosVALLE, MATHEUS del21-11Proceedings6931209914464841115209ZEZELL, DENISE M.:693:920:NBERNARDES, EMERSON S.:12099:110:NSANTOS, SOFIA N. dos:14464:110:NSANTOS, MOISES O. dos:8411:920:NVALLE, MATHEUS del:15209:920:SLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.ipen.br/bitstream/123456789/32334/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52ORIGINAL28102.pdf28102.pdfapplication/pdf316112http://repositorio.ipen.br/bitstream/123456789/32334/1/28102.pdfc79d2fcfb58a82462cbca62ddb656194MD51123456789/323342021-12-02 18:00:40.73oai:repositorio.ipen.br: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Repositório InstitucionalPUBhttp://repositorio.ipen.br/oai/requestbibl@ipen.bropendoar:45102021-12-02T18:00:40Repositório Institucional do IPEN - Instituto de Pesquisas Energéticas e Nucleares (IPEN)false
dc.title.pt_BR.fl_str_mv Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images
title Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images
spellingShingle Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images
VALLE, MATHEUS del
mammary glands
neoplasms
fourier transformation
images
machine learning
hormones
receptors
title_short Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images
title_full Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images
title_fullStr Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images
title_full_unstemmed Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images
title_sort Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images
author VALLE, MATHEUS del
author_facet VALLE, MATHEUS del
SANTOS, MOISES O. dos
SANTOS, SOFIA N. dos
BERNARDES, EMERSON S.
ZEZELL, DENISE M.
SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE
author_role author
author2 SANTOS, MOISES O. dos
SANTOS, SOFIA N. dos
BERNARDES, EMERSON S.
ZEZELL, DENISE M.
SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv VALLE, MATHEUS del
SANTOS, MOISES O. dos
SANTOS, SOFIA N. dos
BERNARDES, EMERSON S.
ZEZELL, DENISE M.
SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE
dc.subject.por.fl_str_mv mammary glands
neoplasms
fourier transformation
images
machine learning
hormones
receptors
topic mammary glands
neoplasms
fourier transformation
images
machine learning
hormones
receptors
description The breast cancer is the most incident cancer in women. Evaluation of hormone receptors expression plays an important role to outline treatment strategies. FTIR spectroscopy imaging may be employed as an additional technique, providing extra information to help physicians. In this work, estrogen and progesterone receptors expression were evaluated using tumors biopsies from human cell lines inoculated in mice. FTIR images were collect from histological sections, and six machine learning models were applied and assessed. Xtreme gradient boost and Linear Discriminant Analysis presented the best accuracies results, indicating to be potential models for breast cancer classification tasks.
publishDate 2021
dc.date.evento.pt_BR.fl_str_mv May 31 - June 2, 2021
dc.date.accessioned.fl_str_mv 2021-11-12T10:59:23Z
dc.date.available.fl_str_mv 2021-11-12T10:59:23Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://repositorio.ipen.br/handle/123456789/32334
dc.identifier.doi.pt_BR.fl_str_mv 10.1109/SBFOTONIOPC50774.2021.9461946
dc.identifier.orcid.pt_BR.fl_str_mv 0000-0001-7404-9606
0000-0002-0029-7313
url http://repositorio.ipen.br/handle/123456789/32334
identifier_str_mv 10.1109/SBFOTONIOPC50774.2021.9461946
0000-0001-7404-9606
0000-0002-0029-7313
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dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
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