Intra-operative Detection of Positive Margins and Lymph Nodes in Breast Surgery
Multimodal Spectroscopic Imagining for Intra-operative Assessment in Breast Cancer Surgery.
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.
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
The new optical coherence tomography (OCT)-Raman spectroscopy system developed in this project will integrate both modalities into a single instrument and employ deep learning algorithms for automated data acquisition and analysis. The OCT module will be designed for rapid scanning of large lumpectomy specimens, including automatic focus adjustment for irregular three-dimensional tissue surfaces. Machine learning (ML) algorithms will identify regions of interest (non-adipose tissue) in the OCT images and automatically direct Raman spectroscopy measurements to these high-risk areas. A second layer of machine learning models will then classify the Raman spectra to distinguish cancerous tissue (positive margins) from benign tissue.
This integrated approach simplifies operation, reduces user subjectivity, and minimises training requirements. The user will only need to place the specimen into the instrument, after which all subsequent steps-including OCT imaging, Raman spectroscopy measurements, data analysis, and image reconstruction-will be performed automatically. The final output will be a diagnostic map highlighting any positive surgical margins in red. By combining rapid OCT imaging with the molecular specificity of Raman spectroscopy, the system aims to translate the high diagnostic accuracy of Raman spectroscopy from millimetre-scale sampling to whole-specimen assessment, providing surgeons with a practical tool for intra-operative margin evaluation.
The OCT-Raman device used in this study has been developed by the University of Nottingham. This is a single-centre proof-of-concept study of an in-house developed device. The results generated will be used solely to evaluate the performance of the device and will not be used to direct or influence participants' clinical care.
In addition, the project will be extended to include the analysis of sentinel lymph nodes using an autofluorescence (AF)-Raman spectroscopy system. This system integrates autofluorescence imaging and Raman spectroscopy into a single device for lymph node assessment, combining the high imaging speed and spatial resolution of autofluorescence with the molecular specificity of Raman spectroscopy. Data acquisition and analysis will be performed using automated deep learning algorithms. The University of Nottingham team has previously demonstrated this concept in an autofluorescence-Raman spectroscopy instrument developed for detecting positive margins during Mohs micrographic surgery for skin cancer. In a proof-of-concept study conducted at Nottingham University Hospitals NHS Trust, the device achieved greater than 95% sensitivity and greater than 95% specificity, with total scanning times of 20-30 minutes, while preserving tissue integrity for subsequent histopathological examination.
Once the system has been calibrated and trained to distinguish tumour tissue from normal tissue, the surface of each specimen will be scanned without direct handling of the tissue before being returned to the pathologist for routine clinical processing. The tissue specimens used for clinical diagnosis will not be used for research purposes. Any identifiable patient information will be accessible only to members of the clinical care team, and all samples will remain fully anonymized to researchers who are not involved in the participants' clinical care.
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Ioan Notingher
- Phone Number: 951 5374 0)115 951 3082
- Email: ppzin@exmail.nottingham.ac.uk
Study Contact Backup
- Name: Nehal Atallah
- Phone Number: 07521100084
- Email: msznma@nottingham.ac.uk
Study Locations
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Nottingham, United Kingdom
- Recruiting
- Nottingham University Hospitals
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Contact:
- Nehal F Y Atallah, PhD
- Phone Number: 951 5374 07521100084
- Email: msznma@nottingham.ac.uk
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Contact:
- Dalia F Y Mehaisi, PhD
- Phone Number: 07423481655
- Email: dalia.mehaisi@nottingham.ac.uk
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion criteria
- Patients undergoing breast surgery wide local excision (WLE).
- Able to give informed consent.
- Any age.
Exclusion criteria
• Patients where there is any doubt regarding the diagnosis from pathologist as ascertained by previous diagnostic biopsy.
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Design and build unique OCT and AF-Raman system with integrated machine learning algorithms.
Time Frame: 12 months
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To design and develop OCT-Raman and AF-Raman imaging systems, including their hardware and software architectures with integrated machine learning algorithms, and install both prototypes at UON for evaluation.
The OCT-Raman system will be used to distinguish cancerous from normal breast tissue in lumpectomy and mastectomy specimens, while the AF-Raman system will be used to distinguish metastatic lymph nodes from normal lymphoid tissue.
In each study, tissue samples will be scanned independently, and the measured area will be recorded in mm² for each specimen.
Average Raman spectral intensities will then be calculated separately for cancerous and normal tissues, and a t-test will be performed to identify the Raman bands exhibiting the most significant differences between the two tissue types.
Data acquisition, area measurements, and statistical analyses for the two experiments will be conducted independently by separate investigators.
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12 months
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Feasibility and Diagnostic Performance of Intraoperative Raman Spectroscopy
Time Frame: 30 months
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Secondary endpoint is the assembly of the Raman devices in the clinical intraoperative theatre for a quick and reliable measures of wide local excisions and lymph node specimens within short period of time (10-20 minutes).
The desired end point will be detecting cancer cells with both sensitivity and specificity higher than 95%.
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30 months
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Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Ioan Notingher, University of Nottingham
- Principal Investigator: Andrew Green, University of Nottingham
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- 336788
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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