Recognition of breast cancer subtypes using FTIR hyperspectral data

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Molecular and biomolecular spectroscopy, 2024-04, Vol.310, p.123941, Article 123941
• Main Authors: Farooq, Sajid, del-Valle, Matheus, dos Santos, Sofia Nascimento, Bernardes, Emerson Soares, Zezell, Denise Maria
• Format: Article
• Language: English
• Subjects: Breast cancer; Discriminant analysis; FTIR; Hyperspectral data
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2024.123941

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. [Display omitted] •Breast cancer subtypes were evaluated by micro-FTIR and 3D discriminant analysis.•3D discriminant analysis algorithm significantly improved discrimination performance.•Proposed method accuracy is over 98%, in opposition to 85% conventional unfold one.•3D-DA provided biochemical signatures in BC that may be used for tailored treatments.