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Recognition of breast cancer subtypes using FTIR hyperspectral data.
Farooq, Sajid; Del-Valle, Matheus; Dos Santos, Sofia Nascimento; Bernardes, Emerson Soares; Zezell, Denise Maria.
Afiliación
  • Farooq S; Center for Lasers and Applications, Instituto de Pesquisas Energeticas e Nucleares, IPEN-CNEN, Address One, Sao Paulo, 05508-000, Sao Paulo, Brazil.
  • Del-Valle M; Center for Lasers and Applications, Instituto de Pesquisas Energeticas e Nucleares, IPEN-CNEN, Address One, Sao Paulo, 05508-000, Sao Paulo, Brazil.
  • Dos Santos SN; Center for Radiopharmaceutics, Instituto de Pesquisas Energeticas e Nucleares, IPEN-CNEN, Address One, Sao Paulo, 05508-000, Sao Paulo, Brazil.
  • Bernardes ES; Center for Radiopharmaceutics, Instituto de Pesquisas Energeticas e Nucleares, IPEN-CNEN, Address One, Sao Paulo, 05508-000, Sao Paulo, Brazil.
  • Zezell DM; Center for Lasers and Applications, Instituto de Pesquisas Energeticas e Nucleares, IPEN-CNEN, Address One, Sao Paulo, 05508-000, Sao Paulo, Brazil. Electronic address: zezell@usp.br.
Spectrochim Acta A Mol Biomol Spectrosc ; 310: 123941, 2024 Apr 05.
Article en En | MEDLINE | ID: mdl-38290283
ABSTRACT
Fourier-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 non-luminal (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-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Female / Humans Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Reino Unido
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Female / Humans Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Reino Unido