Machine learning in combined multi-separation diffuse reflectance and intrinsic fluorescence spectroscopy diagnostics of breast tumours

Vadzim Chalau; Dhurka Shanthakumar; Ioannis Gkouzionis; Yufeng Shi; Maria Leiloglou; Anna Silvanto; Daniel R. Leff; Daniel S. Elson

doi:10.1117/12.3022183

19 June 2024 Machine learning in combined multi-separation diffuse reflectance and intrinsic fluorescence spectroscopy diagnostics of breast tumours

Vadzim Chalau, Dhurka Shanthakumar, Ioannis Gkouzionis, Yufeng Shi, Maria Leiloglou, Anna Silvanto, Daniel R. Leff, Daniel S. Elson

Author Affiliations +

Proceedings Volume PC13009, Clinical Biophotonics III; PC130090J (2024) https://doi.org/10.1117/12.3022183
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

The high reoperation rate after breast-conserving surgery (in average 19% in the UK) is associated with the lack of efficient and easy to apply intraoperative methods for detection the tumour residue (“positive margin”) of the excised sample. In-situ tests, based on diffuse reflectance and intrinsic fluorescence spectroscopy could potentially palliate this problem by interrogating tissues at a depth of up to several millimetres. We evaluated three machine learning algorithms applied to a dataset of diffuse reflectance and fluorescence spectra consisting of 181 frozen breast samples, collected from 138 patients. The diagnostic accuracy depended on the applied algorithm and the AUCs ranged from 0.71 to 0.81 (maximal sensitivity 86.16%, specificity 58.97%) and is comparable with existent intraoperational modalities, such as, for example, MarginProbe. Further research is needed to find an optimal combination of spectral features and diagnostic algorithm.

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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Vadzim Chalau, Dhurka Shanthakumar, Ioannis Gkouzionis, Yufeng Shi, Maria Leiloglou, Anna Silvanto, Daniel R. Leff, and Daniel S. Elson "Machine learning in combined multi-separation diffuse reflectance and intrinsic fluorescence spectroscopy diagnostics of breast tumours", Proc. SPIE PC13009, Clinical Biophotonics III, PC130090J (19 June 2024); https://doi.org/10.1117/12.3022183

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KEYWORDS

Diagnostics

Machine learning

Diffuse reflectance spectroscopy

Breast

Fluorescence spectroscopy

Tissues

Surgery

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