Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods

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dc.contributor.authorFAROOQ, SAJIDpt_BR
dc.contributor.authorDEL-VALLE, MATHEUSpt_BR
dc.contributor.authorSANTOS, MOISES O. dospt_BR
dc.contributor.authorSANTOS, SOFIA N. dospt_BR
dc.contributor.authorBERNARDES, EMERSON S.pt_BR
dc.coverageInternacional
dc.date.accessioned2023-07-24T14:21:37Z
dc.date.available2023-07-24T14:21:37Z
dc.date.issued2023pt_BR
dc.description.abstractBreast cancer (BC) molecular subtypes diagnosis involves improving clinical uptake by Fourier transform infrared (FTIR) spectroscopic imaging, which is a non-destructive and powerful technique, enabling label free extraction of biochemical information towards prognostic stratification and evaluation of cell functionality. However, methods of measurements of samples demand a long time to achieve high quality images, making its clinical use impractical because of the data acquisition speed, poor signal to noise ratio, and deficiency of optimized computational framework procedures. To address those challenges, machine learning (ML) tools can facilitate obtaining an accurate classification of BC subtypes with high actionability and accuracy. Here, we propose a ML-algorithmbased method to distinguish computationally BC cell lines. The method is developed by coupling the K-neighbors classifier (KNN) with neighborhood components analysis (NCA), and hence, the NCA-KNN method enables to identify BC subtypes without increasing model size as well as adding additional computational parameters. By incorporating FTIR imaging data, we show that classification accuracy, specificity, and sensitivity improve, respectively, 97.5%, 96.3%, and 98.2%, even at very low co-added scans and short acquisition times. Moreover, a clear distinctive accuracy (up to 9 %) difference of our proposed method (NCA-KNN) was obtained in comparison with the second best supervised support vector machine model. Our results suggest a key diagnostic NCA-KNN method for BC subtypes classification that may translate to advancement of its consolidation in subtype-associated therapeutics.pt_BR
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)pt_BR
dc.description.sponsorshipIDFAPESP: 17/50332-0; 21/00633-0pt_BR
dc.description.sponsorshipIDCNPq: [465763/2014-6; 440228/2021-2; 314517/2021-9pt_BR
dc.description.sponsorshipIDCAPES: 001pt_BR
dc.format.extentC80 - C87pt_BR
dc.identifier.citationFAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, MOISES O. dos; SANTOS, SOFIA N. dos; BERNARDES, EMERSON S. Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods. <b>Applied Optics</b>, v. 62, n. 8, p. C80 - C87, 2023. DOI: <a href="https://dx.doi.org/10.1364/AO.477409">10.1364/AO.477409</a>. Disponível em: http://repositorio.ipen.br/handle/123456789/34165.
dc.identifier.doi10.1364/AO.477409pt_BR
dc.identifier.fasciculo8pt_BR
dc.identifier.issn1559-128X
dc.identifier.orcid0000-0002-0029-7313pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-0029-7313
dc.identifier.percentilfi39.1
dc.identifier.percentilfiCiteScore57.67
dc.identifier.urihttp://repositorio.ipen.br/handle/123456789/34165
dc.identifier.vol62pt_BR
dc.relation.ispartofApplied Optics
dc.rightsopenAccesspt_BR
dc.subjectdiagnosis
dc.subjectdiagnostic techniques
dc.subjectneoplasms
dc.subjectmammary glands
dc.subjectfourier transformation
dc.subjectinfrared spectrometers
dc.subjectmachine learning
dc.titleRapid identification of breast cancer subtypes using micro-FTIR and machine learning methodspt_BR
dc.typeArtigo de periódicopt_BR
dspace.entity.typePublication
ipen.autorSAJID FAROOQ
ipen.autorSOFIA NASCIMENTO DOS SANTOS
ipen.autorEMERSON SOARES BERNARDES
ipen.autorMATHEUS DEL VALLE
ipen.codigoautor15722
ipen.codigoautor14464
ipen.codigoautor12099
ipen.codigoautor15209
ipen.contributor.ipenauthorSAJID FAROOQ
ipen.contributor.ipenauthorSOFIA NASCIMENTO DOS SANTOS
ipen.contributor.ipenauthorEMERSON SOARES BERNARDES
ipen.contributor.ipenauthorMATHEUS DEL VALLE
ipen.date.recebimento23-07
ipen.identifier.fi1.7
ipen.identifier.fiCiteScore3.7
ipen.identifier.ipendoc29788
ipen.identifier.iwosWoSpt_BR
ipen.identifier.ods3
ipen.range.fi1.500 - 2.999
ipen.range.percentilfi25.00 - 49.99
ipen.type.genreArtigo
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relation.isAuthorOfPublication8115c8bd-822c-4f5a-9f49-3c12570ed40a
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relation.isAuthorOfPublication.latestForDiscovery60d3fba4-40e1-482c-9eda-4530bc63fecb
sigepi.autor.atividadeBERNARDES, EMERSON S.:12099:110:Npt_BR
sigepi.autor.atividadeSANTOS, SOFIA N. dos:14464:110:Npt_BR
sigepi.autor.atividadeDEL-VALLE, MATHEUS:15209:920:Npt_BR
sigepi.autor.atividadeFAROOQ, SAJID:15722:920:Spt_BR
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