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Breast Cancer Diagnosis Using Extended-Wavelength–Diffuse Reflectance Spectroscopy (EW-DRS) : Proof of Concept in Ex Vivo Breast Specimens Using Machine Learning

Chaudhry, Nadia LU ; Albinsson, John LU orcid ; Cinthio, Magnus LU orcid ; Kröll, Stefan LU ; Malmsjö, Malin LU orcid ; Rydén, Lisa LU orcid ; Sheikh, Rafi LU orcid ; Reistad, Nina LU orcid and Zackrisson, Sophia LU (2023) In Diagnostics 13(19). p.1-12
Abstract
This study aims to investigate the feasibility of using diffuse reflectance spectroscopy (DRS) to distinguish malignant breast tissue from adjacent healthy tissue, and to evaluate if an extended-wavelength range (450–1550 nm) has an advantage over the standard wavelength range (450–900 nm). Multivariate statistics and machine learning algorithms, either linear discriminant analysis (LDA) or support vector machine (SVM) are used to distinguish the two tissue types in breast specimens (total or partial mastectomy) from 23 female patients with primary breast cancer. EW-DRS has a sensitivity of 94% and specificity of 91% as compared to a sensitivity of 40% and specificity of 71% using the standard wavelength range. The results suggest that DRS... (More)
This study aims to investigate the feasibility of using diffuse reflectance spectroscopy (DRS) to distinguish malignant breast tissue from adjacent healthy tissue, and to evaluate if an extended-wavelength range (450–1550 nm) has an advantage over the standard wavelength range (450–900 nm). Multivariate statistics and machine learning algorithms, either linear discriminant analysis (LDA) or support vector machine (SVM) are used to distinguish the two tissue types in breast specimens (total or partial mastectomy) from 23 female patients with primary breast cancer. EW-DRS has a sensitivity of 94% and specificity of 91% as compared to a sensitivity of 40% and specificity of 71% using the standard wavelength range. The results suggest that DRS can discriminate between malignant and healthy breast tissue, with improved outcomes using an extended wavelength. It is also possible to construct a simple analytical model to improve the diagnostic performance of the DRS technique. (Less)
Abstract (Swedish)
This study aims to investigate the feasibility of using diffuse reflectance spectroscopy (DRS) to distinguish malignant breast tissue from adjacent healthy tissue, and to evaluate if an extended-wavelength range (450–1550 nm) has an advantage over the standard wavelength range (450–900 nm). Multivariate statistics and machine learning algorithms, either linear discriminant analysis (LDA) or support vector machine (SVM) are used to distinguish the two tissue types in breast specimens (total or partial mastectomy) from 23 female patients with primary breast cancer. EW-DRS has a sensitivity of 94% and specificity of 91% as compared to a sensitivity of 40% and specificity of 71% using the standard wavelength range. The results suggest that DRS... (More)
This study aims to investigate the feasibility of using diffuse reflectance spectroscopy (DRS) to distinguish malignant breast tissue from adjacent healthy tissue, and to evaluate if an extended-wavelength range (450–1550 nm) has an advantage over the standard wavelength range (450–900 nm). Multivariate statistics and machine learning algorithms, either linear discriminant analysis (LDA) or support vector machine (SVM) are used to distinguish the two tissue types in breast specimens (total or partial mastectomy) from 23 female patients with primary breast cancer. EW-DRS has a sensitivity of 94% and specificity of 91% as compared to a sensitivity of 40% and specificity of 71% using the standard wavelength range. The results suggest that DRS can discriminate between malignant and healthy breast tissue, with improved outcomes using an extended wavelength. It is also possible to construct a simple analytical model to improve the diagnostic performance of the DRS technique. (Less)
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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
breast cancer, diffuse reflectance spectroscopy, extended-wavelength–diffuse reflectance spectroscopy, linear discriminant analysis, machine learning, support vector machine
in
Diagnostics
volume
13
issue
19
pages
1 - 12
publisher
MDPI AG
external identifiers
  • scopus:85173650411
  • pmid:37835819
ISSN
2075-4418
DOI
10.3390/diagnostics13193076
language
English
LU publication?
yes
id
f941ba0d-e599-458c-bf38-abc713cc8b3c
date added to LUP
2023-09-29 09:07:00
date last changed
2025-04-04 14:27:36
@article{f941ba0d-e599-458c-bf38-abc713cc8b3c,
  abstract     = {{This study aims to investigate the feasibility of using diffuse reflectance spectroscopy (DRS) to distinguish malignant breast tissue from adjacent healthy tissue, and to evaluate if an extended-wavelength range (450–1550 nm) has an advantage over the standard wavelength range (450–900 nm). Multivariate statistics and machine learning algorithms, either linear discriminant analysis (LDA) or support vector machine (SVM) are used to distinguish the two tissue types in breast specimens (total or partial mastectomy) from 23 female patients with primary breast cancer. EW-DRS has a sensitivity of 94% and specificity of 91% as compared to a sensitivity of 40% and specificity of 71% using the standard wavelength range. The results suggest that DRS can discriminate between malignant and healthy breast tissue, with improved outcomes using an extended wavelength. It is also possible to construct a simple analytical model to improve the diagnostic performance of the DRS technique.}},
  author       = {{Chaudhry, Nadia and Albinsson, John and Cinthio, Magnus and Kröll, Stefan and Malmsjö, Malin and Rydén, Lisa and Sheikh, Rafi and Reistad, Nina and Zackrisson, Sophia}},
  issn         = {{2075-4418}},
  keywords     = {{breast cancer; diffuse reflectance spectroscopy; extended-wavelength–diffuse reflectance spectroscopy; linear discriminant analysis; machine learning; support vector machine}},
  language     = {{eng}},
  number       = {{19}},
  pages        = {{1--12}},
  publisher    = {{MDPI AG}},
  series       = {{Diagnostics}},
  title        = {{Breast Cancer Diagnosis Using Extended-Wavelength–Diffuse Reflectance Spectroscopy (EW-DRS) : Proof of Concept in Ex Vivo Breast Specimens Using Machine Learning}},
  url          = {{http://dx.doi.org/10.3390/diagnostics13193076}},
  doi          = {{10.3390/diagnostics13193076}},
  volume       = {{13}},
  year         = {{2023}},
}