Intra-operative Detection of Positive Margins and Lymph Nodes in Breast Surgery

Conditions studied

Breast Cancer Invasive

About this study

In this project, the investigators will develop novel optical coherence tomography (OCT)-Raman spectroscopy and autofluorescence (AF)-Raman spectroscopy systems based on a selective sampling approach optimised for high-resolution analysis of whole lumpectomy specimens and sentinel lymph node (SLN) biopsies, respectively. The aim of using optical coherence tomography is not to detect cancer directly, but rather to identify adipose tissue so that large adipose regions can be excluded from subsequent Raman spectroscopy measurements. Although OCT has limited ability to distinguish tumour tissue from the surrounding normal stroma, adipose tissue exhibits a distinctive appearance in optical coherence tomography images because of its low backscattering properties, resulting from adipocytes that are filled with lipids and contain small, flattened nuclei. In contrast, benign dense tissue (stroma, ducts, and lobules) and malignant tissue produce much stronger backscattering signals. These characteristic patterns enable adipose tissue to be distinguished from other breast tissues using classification models based on optical coherence tomography reflectivity profiles, achieving 94% sensitivity and 93% specificity. Excluding adipose tissue from further analysis reduces the number of Raman spectroscopy measurements required, allowing the remaining, smaller tissue regions to be examined to discriminate between benign and malignant tissue. This flexible and adaptable scanning strategy is expected to improve both diagnostic accuracy and scanning speed, enabling complete assessment of surgical margins within clinically practical timescales. In addition, the investigators will develop a novel (AF)-Raman spectroscopy system based on a selective sampling approach optimised for high-resolution analysis of sentinel lymph node specimens. The purpose of incorporating autofluorescence imaging is to identify the optimal sampling locations for subsequent Raman spectroscopy measurements, thereby improving the efficiency of tissue interrogation while maintaining diagnostic accuracy.

Interventions

• Diagnostic Test: OCT-Raman — a machine to detect positive margins in lumpectomy specimens
• Diagnostic Test: AF-Raman — a machine for detection sentinel lymph nodes in Breast conserving surgery.

Primary outcomes

• Design and build unique OCT and AF-Raman system with integrated machine learning algorithms. (12 months)

Eligibility information

Study locations

• Nottingham university hospitals, Nottingham, United Kingdom