Intra-operative Detection of Positive Margins in Breast Surgery

NCT06977698 | Recruiting

• 1 other identifier: 336788
• Study Type: observational
• Target: 120
• Locations: 1 country
• Sites: 1

Brief Summary

In this project, we will develop a unique OCT-Raman system based on a selective sampling approach optimised for high-resolution analysis of whole lumpectomy specimens. The aim of using OCT is not to detect the cancer but to identify the adipose tissue, such that the large adipose tissue regions are excluded from any further measurements by Raman spectroscopy. While OCT has a limited ability to distinguish between tumour and surrounding normal stroma, adipose tissue has a distinctive appearance in the OCT images due to low backscattering within adipose cells (filled with lipids and small/flattened nuclei) compared to the highly scattering benign dense tissue (stroma, ducts and lobules) and malignant tissue. Such specific patterns allow identification of normal adipose tissue from breast tissue (classification models based on reflectivity profiles) with 94% sensitivity and 93% specificity. This will reduce the task of Raman measurements, which can be focused on the smaller remaining regions to discriminate between the benign and malignant tissue. This flexible and adaptable scanning strategy will achieve a much-improved diagnosis accuracy and speed to cover all surgical margins within practical timescales.

Conditions

Breast Cancer Invasive

Keywords

Breast cancer; Margins

Outcome Measures

Primary Outcomes (1)

• Design and build a unique OCT-Raman system with integrated machine learning

  The primary endpoint is to design the instrument and overall architecture (hardware/software). This includes basic machine learning algorithms for the OCT and OCT-Raman combination. In addition, to install the primary first OCT prototype in UON and test it on lumpectomy and or mastectomy specimen to identify residual cancer cellscancer from normal tissue

  12 months

Interventions

OCT-Raman DIAGNOSTIC_TEST

a machine to detect positive margins in lumpectomy specimens

Eligibility Criteria

• Sex: female
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients undergoing WLE in the breast cancer institute

You may qualify if:

• Patients undergoing breast surgery (wide local excision).
• Able to give informed consent.
• Any age.

You may not qualify if:

• Patients where there is any doubt regarding the diagnosis from pathologist as ascertained by previous diagnostic biopsy.

Sponsors & Collaborators

• University of Nottingham: lead

Study Sites (1)

Nottingham university hospitals

Nottingham, United Kingdom

RECRUITING

MeSH Terms

Conditions

Breast Neoplasms; Margins of Excision

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases; Morphological and Microscopic Findings; Pathological Conditions, Signs and Symptoms

Study Officials

• Ioan Notingher

  University of Nottingham

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Ioan Notingher

CONTACT

0)115 951 3082; ppzin@exmail.nottingham.ac.uk

Nehal Atallah

CONTACT

07521100084; msznma@nottingham.ac.uk

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 9, 2025
• First Posted: May 18, 2025
• Study Start: October 1, 2023
• Primary Completion (Estimated): April 1, 2027
• Study Completion (Estimated): April 1, 2027
• Last Updated: May 18, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)