Breast Cancer InvasiveNCT06977698
Intra-operative Detection of Positive Margins and Lymph Nodes in Breast Surgery
An observational study of Breast Cancer Invasive, run by University of Nottingham at 1 site in United Kingdom.
Trial summary
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… Read more about this study
- Who is it for?
- Any age, women only
- Conditions the study is for
- Breast Cancer Invasive
- What is being tested?
- OCT-Raman and AF-Raman
- How many people
- 120 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 Oct 2023to 1 Apr 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- Women only
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients undergoing Wide Local Excision (WLE) for breast cancer at the Breast Cancer Institute, Nottingham City Hospital.
Who may be able to join
- Patients undergoing breast surgery wide local excision (WLE).
- Able to give informed consent.
- Any age.
Who cannot join
- Patients where there is any doubt regarding the diagnosis from pathologist as ascertained by previous diagnostic biopsy.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United Kingdom1 site, 1 recruiting
- Recruiting
Nottingham university hospitals
Nottingham
Nehal F Y Atallah, PhD, 07521100084 ext. 951 5374, msznma@nottingham.ac.uk
Dalia F Y Mehaisi, PhD, 07423481655, dalia.mehaisi@nottingham.ac.uk
About this study
Official title: 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.
Read the 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.
- Conditions
- Breast Cancer Invasive
- Being tested
- OCT-Raman, AF-Raman
- Keywords
- Breast cancer, Margins, Breast conserving surgery, Raman Spectroscopy, Sentinel Lymph Node, computational pathology., intra-operative imaging, Autofluorescence
- Medical subject headings
- Breast Neoplasms, Margins of Excision
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
OCT-RamanDiagnostic test
a machine to detect positive margins in lumpectomy specimens
AF-RamanDiagnostic test
a machine for detection sentinel lymph nodes in Breast conserving surgery.
What is being measured
The primary outcome is the main question the study is designed to answer.
Design and build unique OCT and AF-Raman system with integrated machine learning algorithms.
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.
Measured: 12 months
Secondary outcomes (1)
Feasibility and Diagnostic Performance of Intraoperative Raman Spectroscopy
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%.
Measured: 30 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 May 2025
- Study start1 Oct 2023
- Primary completion1 Apr 2027 (estimated)
- Study completion1 Apr 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast Cancer InvasiveStudies of the same condition, open and under way.
- More trials run by University of NottinghamThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Nottingham
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Ioan Notingher, Principal investigator, University of NottinghamAndrew Green, Principal investigator, University of Nottingham
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06977698
- Sponsor's protocol ID
- 336788
- Status last verified
- Jun 2026
- First submitted
- 9 May 2025
- Last updated
- 30 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 30 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal Spectroscopic Imagining for Intra-operative Assessment in Breast Cancer Surgery.
- ClinicalTrials.gov ID
- NCT06977698
- Acronym / sponsor ID
- 336788
- Conditions
- Breast Cancer Invasive
- Keywords
- Breast cancer, Margins, Breast conserving surgery, Raman Spectroscopy, Sentinel Lymph Node, computational pathology., intra-operative imaging, Autofluorescence
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- University of NottinghamUniversity, hospital or other
- Study start
- 1 Oct 2023 (actual)
- Primary completion
- 1 Apr 2027 (estimated)
- Study completion
- 1 Apr 2027 (estimated)
- First posted
- 18 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Case only
- Time perspective
- Cross-sectional
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients undergoing Wide Local Excision (WLE) for breast cancer at the Breast Cancer Institute, Nottingham City Hospital.
Key inclusion criteria (3 listed)
- Patients undergoing breast surgery wide local excision (WLE).
- Able to give informed consent.
- Any age.
Key exclusion criteria (1 listed)
- Patients where there is any doubt regarding the diagnosis from pathologist as ascertained by previous diagnostic biopsy.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| OCT-Ramana machine to detect positive margins in lumpectomy specimens | Diagnostic test | – |
| AF-Ramana machine for detection sentinel lymph nodes in Breast conserving surgery. | Diagnostic test | – |
Primary outcome
Design and build unique OCT and AF-Raman system with integrated machine learning algorithms.
Time frame: 12 months
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.
1 secondary outcome
Feasibility and Diagnostic Performance of Intraoperative Raman Spectroscopy
Time frame: 30 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Jul 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Ioan Notingher principal investigator
University of Nottingham
Andrew Green principal investigator
University of Nottingham
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer Invasive, run by University of Nottingham at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 30 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ioan Notingher (0)115 951 3082 ext. 951 5374, ppzin@exmail.nottingham.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Design and build unique OCT and AF-Raman system with integrated machine learning algorithms. (time frame: 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Jul 2026.