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Margin assessment during breast conserving surgery using diffuse reflectance spectroscopy.

Dinusha Veluponnar^1,2, Lisanne L de Boer¹, Behdad Dashtbozorg¹

¹Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands.

Journal of Biomedical Optics

|April 26, 2024

Summary

This summary is machine-generated.

Diffuse reflectance spectroscopy (DRS) enables real-time margin assessment during breast cancer surgery. This technique accurately distinguishes tumorous from healthy tissue, improving surgical outcomes.

Keywords:

breast-conserving surgery diffuse reflectance spectroscopy resection margin assessment tissue classification

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Area of Science:

Biomedical Optics
Surgical Oncology
Medical Diagnostics

Background:

Accurate evaluation of resection margins is crucial in breast-conserving surgeries to ensure complete tumor removal.
Current methods lack real-time capabilities for precise margin assessment during surgery.

Purpose of the Study:

To investigate the diagnostic accuracy of diffuse reflectance spectroscopy (DRS) for real-time margin evaluation in breast-conserving surgery.
To assess the ability of DRS combined with tissue classification models to differentiate tumorous from healthy breast tissue at the resection margin.

Main Methods:

Collected diffuse reflectance spectroscopy (DRS) data from both ex vivo and in vivo breast tissue.
Developed and optimized tissue classification models using the collected DRS data.
Validated the performance of DRS for in vivo tissue discrimination during breast-conserving surgery.

Main Results:

Achieved a high diagnostic performance with a Matthews correlation coefficient of 0.76.
Demonstrated excellent sensitivity (96.7%) and specificity (90.6%) for tumorous tissue detection.
Obtained an area under the curve (AUC) of 0.98, indicating strong model accuracy.

Conclusions:

Diffuse reflectance spectroscopy (DRS) is a viable tool for real-time margin assessment in breast-conserving surgeries.
DRS offers high sensitivity and specificity, aiding surgeons in achieving clear margins.
The developed classification models, trained on combined ex vivo and in vivo data, show significant potential for clinical application.