Diffuse reflectance and fluorescence spectroscopy for breast conserving surgery
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Purpose
The major challenge in breast conserving surgery is the high rates of re-excision due to positive resection margins. This study evaluates whether a combined diffuse reflectance spectroscopy (DRS) and laser induced intrinsic fluorescence spectroscopy (IFS) technique can differentiate breast tissue sample types, towards the development of an intraoperative margin assessment tool.
Methods
Breast tissue samples were collected from patients undergoing breast cancer surgery. A handheld DRS-IFS probe was used on the frozen thawed ex-vivo breast samples to acquire spectral data. Machine learning classifiers were used to determine sensitivity, specificity, overall diagnostic accuracy, and the area under the curve (AUC) against “gold-standard” histopathology ground truth.
Results
181 breast tissue samples from 138 patients were interrogated using DRS-IFS. All patients were female, with median age (range) of 56.8 (20–94) years The total number of spectra acquired was 18,349. Following five-fold cross validation for normal versus cancer tissue, extreme gradient boost classifier achieved a sensitivity of 84% (SD ± 13), specificity of 61% (SD ± 16), overall diagnostic accuracy of 75% (SD ± 3), and AUC of 84%.
Conclusion
The results suggests that DRS-IFS can distinguish normal breast tissue from breast cancer with high diagnostic accuracy. For DRS-IFS to be translated into the operating theatre to aid a surgeon’s real-time visualisation for oncologic margin control assessment of intraoperative, the in vivo diagnostic accuracy needs to be determined.
The major challenge in breast conserving surgery is the high rates of re-excision due to positive resection margins. This study evaluates whether a combined diffuse reflectance spectroscopy (DRS) and laser induced intrinsic fluorescence spectroscopy (IFS) technique can differentiate breast tissue sample types, towards the development of an intraoperative margin assessment tool.
Methods
Breast tissue samples were collected from patients undergoing breast cancer surgery. A handheld DRS-IFS probe was used on the frozen thawed ex-vivo breast samples to acquire spectral data. Machine learning classifiers were used to determine sensitivity, specificity, overall diagnostic accuracy, and the area under the curve (AUC) against “gold-standard” histopathology ground truth.
Results
181 breast tissue samples from 138 patients were interrogated using DRS-IFS. All patients were female, with median age (range) of 56.8 (20–94) years The total number of spectra acquired was 18,349. Following five-fold cross validation for normal versus cancer tissue, extreme gradient boost classifier achieved a sensitivity of 84% (SD ± 13), specificity of 61% (SD ± 16), overall diagnostic accuracy of 75% (SD ± 3), and AUC of 84%.
Conclusion
The results suggests that DRS-IFS can distinguish normal breast tissue from breast cancer with high diagnostic accuracy. For DRS-IFS to be translated into the operating theatre to aid a surgeon’s real-time visualisation for oncologic margin control assessment of intraoperative, the in vivo diagnostic accuracy needs to be determined.
Date Issued
2025-08-01
Date Acceptance
2025-07-16
Citation
Breast Cancer Research and Treatment, 2025, 214 (1), pp.25-36
ISSN
0167-6806
Publisher
Springer
Start Page
25
End Page
36
Journal / Book Title
Breast Cancer Research and Treatment
Volume
214
Issue
1
Copyright Statement
© The Author(s) 2025 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Publication Status
Published
Date Publish Online
2025-08-01