Recognition of breast cancer subtypes using FTIR hyperspectral data

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dc.contributor.authorFAROOQ, SAJID
dc.contributor.authorDEL-VALLE, MATHEUS
dc.contributor.authorSANTOS, SOFIA N. dos
dc.contributor.authorBERNARDES, EMERSON S.
dc.contributor.authorZEZELL, DENISE M.
dc.coverageInternacional
dc.date.accessioned2024-04-15T13:58:36Z
dc.date.available2024-04-15T13:58:36Z
dc.date.issued2024
dc.description.abstractFourier -transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro -environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data -acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and nonluminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three -dimension (3D) -discriminant analysis approach based on 3D -principle component analysis -linear discriminant analysis (3D-PCA-LDA) and 3D -principal component analysis -quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCALDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Ensino Superior (CAPES)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIDCNPq: INCT-INTERAS 406761/2022-1; INCT-INFO 465763/2014-6; Sisfoton 440228/2021-2; PQ 314517/2021-9
dc.description.sponsorshipIDCAPES: 001
dc.description.sponsorshipIDFAPESP: 17/50332-0; 21/00633-0
dc.format.extent1-8
dc.identifier.citationFAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, SOFIA N. dos; BERNARDES, EMERSON S.; ZEZELL, DENISE M. Recognition of breast cancer subtypes using FTIR hyperspectral data. <b>Spectrochimica Acta Part A</b>, v. 310, p. 1-8, 2024. DOI: <a href="https://dx.doi.org/10.1016/j.saa.2024.123941">10.1016/j.saa.2024.123941</a>. Disponível em: https://repositorio.ipen.br/handle/123456789/48043.
dc.identifier.doi10.1016/j.saa.2024.123941
dc.identifier.issn1386-1425
dc.identifier.orcidhttps://orcid.org/0000-0002-0029-7313
dc.identifier.orcidhttps://orcid.org/0000-0001-7404-9606
dc.identifier.percentilfi89.8
dc.identifier.percentilfiCiteScore85.50
dc.identifier.urihttps://repositorio.ipen.br/handle/123456789/48043
dc.identifier.vol310
dc.relation.ispartofSpectrochimica Acta Part A
dc.rightsopenAccess
dc.subjectfourier transform spectrometers
dc.subjectfourier transformation
dc.subjectinfrared spectra
dc.subjectneoplasms
dc.subjectmammary glands
dc.titleRecognition of breast cancer subtypes using FTIR hyperspectral data
dc.typeArtigo de periódico
dspace.entity.typePublication
ipen.autorSAJID FAROOQ
ipen.autorMATHEUS DEL VALLE
ipen.autorSOFIA NASCIMENTO DOS SANTOS
ipen.autorEMERSON SOARES BERNARDES
ipen.autorDENISE MARIA ZEZELL
ipen.codigoautor15722
ipen.codigoautor15209
ipen.codigoautor14464
ipen.codigoautor12099
ipen.codigoautor693
ipen.contributor.ipenauthorSAJID FAROOQ
ipen.contributor.ipenauthorMATHEUS DEL VALLE
ipen.contributor.ipenauthorSOFIA NASCIMENTO DOS SANTOS
ipen.contributor.ipenauthorEMERSON SOARES BERNARDES
ipen.contributor.ipenauthorDENISE MARIA ZEZELL
ipen.identifier.fi4.3
ipen.identifier.fiCiteScore8.4
ipen.identifier.ipendoc30368
ipen.identifier.iwosWoS
ipen.range.fi3.000 - 4.499
ipen.range.percentilfi75.00 - 100.00
ipen.type.genreArtigo
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relation.isAuthorOfPublication.latestForDiscovery60d3fba4-40e1-482c-9eda-4530bc63fecb
sigepi.autor.atividadeSAJID FAROOQ:15722:920:S
sigepi.autor.atividadeMATHEUS DEL VALLE:15209:920:N
sigepi.autor.atividadeSOFIA NASCIMENTO DOS SANTOS:14464:110:N
sigepi.autor.atividadeEMERSON SOARES BERNARDES:12099:110:N
sigepi.autor.atividadeDENISE MARIA ZEZELL:693:920:N
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