Breast Cancer Diagnosis Using Extended-Wavelength–Diffuse Reflectance Spectroscopy (EW-DRS) : Proof of Concept in Ex Vivo Breast Specimens Using Machine Learning
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/f941ba0d-e599-458c-bf38-abc713cc8b3c
- author
- Chaudhry, Nadia LU ; Albinsson, John LU ; Cinthio, Magnus LU ; Kröll, Stefan LU ; Malmsjö, Malin LU ; Rydén, Lisa LU ; Sheikh, Rafi LU ; Reistad, Nina LU and Zackrisson, Sophia LU
- organization
-
- LUCC: Lund University Cancer Centre
- Radiology Diagnostics, Malmö (research group)
- LU Profile Area: Light and Materials
- LTH Profile Area: Photon Science and Technology
- Ophthalmology Imaging Research Group (research group)
- LTH Profile Area: Engineering Health
- LTH Profile Area: Nanoscience and Semiconductor Technology
- Atomic Physics
- NanoLund: Centre for Nanoscience
- Clinical and experimental lung transplantation (research group)
- NPWT technology (research group)
- The Liquid Biopsy and Tumor Progression in Breast Cancer (research group)
- Breast Cancer Surgery (research group)
- EpiHealth: Epidemiology for Health
- publishing date
- 2023
- 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
- 2023-12-30 03:00:17
@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}}, }