Colorectal CancerFOCCuS1NCT04220242
Future of Colorectal Cancer Surgery
An observational study of Colorectal Cancer and Surgery, run by Mater Misericordiae University Hospital at 1 site in Ireland.
Trial summary
Colorectal cancer is the third most common cancer in the UK and Ireland, it is the second commonest cancer in both men and women. Very often the diagnosis is made by either endoscopy/colonoscopy and the surgical treatment is carried out by a minimally invasive approach ("Keyhole"surgery). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Colorectal Cancer, Surgery
- What is being tested?
- Video recordings with analysis thereafter applied, Examination of microsections of tissue excised for the purposes of cancer resection and Cancer organoid development and testing
- How many people
- 250 participants
- Where
- 1 siteIreland
- Study dates
- 30 Dec 2019to 1 Mar 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
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 18 years and older
- All sexes
- Adult, Older adult
Study population: Participants with or suspected of colorectal neoplasia undergoing endoscopic evaluation or laparoscopic resection of same or other colorectal disease by segmental resection and anastomosis. Clinically fit for elective intervention and meeting inclusion/exclusion criteria.
Who may be able to join
- • Participant is willing and able to give informed consent for participation in the study.
- Male or Female, aged 18 years or above.
- Clinical features suspicious of or diagnosed with colorectal neoplasia or other colorectal disease requiring segmental resection with anastomosis.
- No prior allergy to indocyanine green or iodine.
- If female and of potential child-bearing age, have a negative pregnancy test at time of study participation.
- Participant has clinically acceptable laboratory results, including liver function tests.
- In the Investigator's opinion, is able and willing to comply with all study requirements.
- Willing to allow his or her General Practitioner and consultant, if appropriate, to be notified of participation in the study.
Who cannot join
- • Female participant who is pregnant, lactating or planning pregnancy during the course of the study.
- Significant renal or hepatic impairment.
- Any other significant disease or disorder which, in the opinion of the Investigator, may either put the participants at risk because of participation in the study, or may influence the result of the study, or the participant's ability to participate in the study.
Trial locations and contacts
1 site in 1 country.
Ireland1 site, 1 recruiting
- Recruiting
Mater Misericordiae University Hospital
Dublin, Other (Non U.s.), D07 R2WY
Ronan Cahill, 0035317164597, ronan.cahill@ucd.ie
Debbie Killeen, 00353 1 7164597, debbie.killeen@ucd.ie
About this study
Official title: Future of Colorectal Cancer Surgery 1- Development of an Artificial Intelligence Model for the Interpretation of Colorectal Cancer Fluorescence Signalling Using Indocyanine Green
Colorectal cancer is the third most common cancer in the UK and Ireland, it is the second commonest cancer in both men and women. Very often the diagnosis is made by either endoscopy/colonoscopy and the surgical treatment is carried out by a minimally invasive approach ("Keyhole"surgery). Tissue samples gathered by either approach are sent to the pathologist to confirm the nature of their content. At present this takes some time (days) and so the information cannot guide the procedure being done or indeed any other investigations or processes that need implementation as soon as possible until the pathology process is completed. Fluorescence guided surgery uses an approved dye along with approved cameras to add more information regarding tissue characteristics then is available by normal viewing alone. It has already been shown to be associated with an improvement in safety related to healing after colorectal surgery and the investigators are sooning in a randomised trial examining this in rectal cancer to prove it. Whether or not this trial proves this or not, the ability to better understand tissue health during investigation/operation needs further examination and development. In this study, the investigators will examine the role of computer vision and machine learning in determining the nature of the tissue being seen in real-time additive to the surgeons' own opinion and experience. This is needed because the dynamic phases of fluorescence inflow into any tissue is difficult to interpret most especially when it relates to microvasculature as is present within a cancer site or deposit. By this means the investigators hope to better understand the dynamic perfusion in and out of tissue whether normal or abnormal and define signatures that can speed up and/or help inform the surgeon regarding the actual nature of the tissue being seen. The investigators will compare the data being generated with that already being captured with regard to standard pathology and radiology and other laboratory measures of clinical course. Tissue resected from a patient will also be examined in the laboratory under near-infrared microscopy and analysed for fluorescence intensity to understand where exactly and how much of the dye accumulates in specific regions of tissue. There are no new operations in this study and no new interventions are being made on the basis of the information being gathered- it's a comparative study to see how this added information can add value to interventionalists during surgery. There are four collaborating groups involved in this research consortium, two are commercial partners as they add value in both this advanced field of analytics and in the ensuring a clinical business case is included so that findings of this work can be useful for patients.
Read the detailed description
This is a combined retrospective and prospective, unblinded, non-CTIMP, multicentre, observational study to develop and determine methods of applying CV and AI with IFA in surgery for clinical benefit in surgery. Surgery can be performed via a minimally invasive fashion whether by an endoscopic or a laparoscopic or robotic technique (the latter depending on surgeon's preference) as part of either a diagnostic or therapeutic intervention in the standard way based on the patients' clinical need. Either before or during the procedure, a visual contrast agent will be administered by peripheral cannula and the area of interest examined by use of a near-infrared scope to determine presence, persistence and inflow/outflow pathways of the dye. The video image will be subjected to further analysis by computer vision and data analytics for the purposes of elucidating specific patterns enabling machine learning to build algorithms for flow characterisation informed by biophysics and pseudo-anonymised clinical data. The developmental algorithms will be additionally informed by mechanistic work quantifying and localising the fluorescence agent within and around sites of abnormal disease by digital fluorescence scanning and near-infrared microscopy as well as deep characterisation of dye clearance dynamics and local tissue metabolites (particularly acidosis). In addition, some tissue from the resected specimens provided in the course of diagnostic investigation or cancer surgery will be used to develop organoids for the purpose of examining in vitro tumour uptake and distribution of fluorescence agents.
In all 250 patients will be studied over the three-year period, comprising 100 patients undergoing anastomotic construction and 100 undergoing cancer diagnostics/resection. Some patients can be included in both groups). Following development (potentially earlier then above), prospective validation will be performed on approximately 25 patients in each group. The follow-up period ends 30 days after recruitment. The trial will not be blinded to participants, medical staff, or clinical trial staff. The contrast agents used are clinically approved (including indocyanine green) for such use within this study. While the validation component of this work will be performed prospectively, the initial model development will include some data from patients retrospectively who have already undergone similar evaluation.
- Conditions
- Colorectal Cancer, Surgery
- Being tested
- Video recordings with analysis thereafter applied, Examination of microsections of tissue excised for the purposes of cancer resection, Cancer organoid development and testing
- Keywords
- artificial intelligence, colorectal surgery, nearinfrared, perfusion, cancer, anastomosis
- Medical subject headings
- Colorectal Neoplasms, Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Video recordings with analysis thereafter appliedOther
Recordings of surgical assessments in order to develop a model for Artificial Intelligence analysis
Examination of microsections of tissue excised for the purposes of cancer resectionOther
Microscopic examination including by near-infrared examination of some of the tissue excised for the purposes of cancer resection to examine cellular mechanisms of fluorescence uptake and distribution
Cancer organoid development and testingOther
Some tissue will be taken from the specimens excised for the purposes of the cancer operation to develop experimental models of cancer tissue to allow examination of mechanisms of uptake and distribution of the fluorophores in cancer
What is being measured
The primary outcome is the main question the study is designed to answer.
Video recordings of Colorectal Cancer.
Video from colorectal endoscopies and laparoscopies recorded from patients undergoing endoscopic or laparoscopic evaluation of colorectal cancer including at the time of intravenous administration of a fluorophore (indocyanine green).
Measured: 6 months
Analysis of video recordings
Computer vision analysis of fluorescence intensity patterns seen in the videos- i.e. ICG perfusion patterns (including presence, persistence and flow).
Measured: 6 months
Biophysics visualisation software development
Biophysics-based visualisation software development that automatically determines ICG perfusion patterns within the field of view of the video related to different colorectal tissue types (cancer and non-cancer).
Measured: 6 months
Biophysics model training
Results from standard clinical tests including histopathology reports used to inform the software analysis profiles so that specific, significant characterisation signatures reflective of underlying tissue architecture and behaviour (and hence nature) are generated.
Measured: 6 months
Validation of predictive accuracy biophysics-visualisation model
Determinative analysis of accuracy of the biophysics model in the prediction of patient course including standard clinical tests (specficially histopathology and expert opinion). Calculation of accuracy, predictive values (positive and negative) and sensitivity and specificity calculation.
Measured: 3 years
Secondary outcomes (3)
Coefficients of Variation
Comparison of localised quantification of ICG in colorectal tissue between the real-time visualisation algorithm and the locally sampled ICG concentrations.
Measured: 3 years
Microscopic Map of intratumoral fluorophore accumulation
Microscopic examination of fresh tissue sections taken at the time of surgery using nearinfrared microscopy examining sites of ICG distribution in normal and abnormal tissue regions. Relative concentrations of ICG intensity throughout the tissue examination by Near-infrared Digital Scanning
Measured: 3 years
Realtime delineation display of tumour area including margins
Automated display map of tumour area and margins by modelling based on other outcome measures listed including video recording analysis, fluorescence intensity modelling and histopathological reporting along with microscopic analysis of tissue specimens
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jan 2020
- Study start30 Dec 2019 (estimated)
- Primary completion1 Dec 2022 (estimated)
- Study completion1 Mar 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Colorectal CancerStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Other
- Enrolment
- 250 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Mater Misericordiae University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Royal College of Surgeons, Ireland, IBM Research In Ireland
- Responsible party
- Principal investigator, Ronan Cahill, Mater Misericordiae University Hospital
- Investigators
- Ronan Cahill, Principal investigator, University College Dublin
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04220242
- Sponsor's protocol ID
- DT 2018 0240
- Acronym
- FOCCuS1
- Status last verified
- Jan 2020
- First submitted
- 30 Dec 2019
- Last updated
- 7 Jan 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Boland PA, MacAonghusa P, Singaravelu A, McEntee PD, Cucek J, Erzen S, Aigner F, Arezzo A, Burke JP, Hompes R, Tuynman JB, Neary PM, Cahill RA. Artificial intelligence classification of rectal neoplasia by endoscopic fluorescence perfusion analysis. Sci Rep. 2026 Jan 6;16(1):4761. doi: 10.1038/s41598-026-35233-x.
PubMed 41495165Hardy NP, MacAonghusa P, Dalli J, Gallagher G, Epperlein JP, Shields C, Mulsow J, Rogers AC, Brannigan AE, Conneely JB, Neary PM, Cahill RA. Clinical application of machine learning and computer vision to indocyanine green quantification for dynamic intraoperative tissue characterisation: how to do it. Surg Endosc. 2023 Aug;37(8):6361-6370. doi: 10.1007/s00464-023-09963-2. Epub 2023 Mar 9.
PubMed 36894810
Study at a glance
From the registry record, last updated by the sponsor on 7 Jan 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Future of Colorectal Cancer Surgery 1- Development of an Artificial Intelligence Model for the Interpretation of Colorectal Cancer Fluorescence Signalling Using Indocyanine Green
- ClinicalTrials.gov ID
- NCT04220242
- Acronym / sponsor ID
- FOCCuS1 / DT 2018 0240
- Conditions
- Colorectal Cancer, Surgery
- Keywords
- artificial intelligence, colorectal surgery, nearinfrared, perfusion, cancer, anastomosis
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 250 participants (estimated)
- Lead sponsor
- Mater Misericordiae University HospitalUniversity, hospital or other
- Collaborators
- Royal College of Surgeons, Ireland, IBM Research In Ireland
- Study start
- 30 Dec 2019 (estimated)
- Primary completion
- 1 Dec 2022 (estimated)
- Study completion
- 1 Mar 2023 (estimated)
- First posted
- 7 Jan 2020 (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.
Colorectal cancer is the third most common cancer in the UK and Ireland, it is the second commonest cancer in both men and women. Very often the diagnosis is made by either endoscopy/colonoscopy and the surgical treatment is carried out by a minimally invasive approach ("Keyhole"surgery). Tissue samples gathered by either approach are sent to the pathologist to confirm the nature of their content. At present this takes some time (days) and so the information cannot guide the procedure being done or indeed any other investigations or processes that need implementation as soon as possible until the pathology process is completed. Fluorescence guided surgery uses an approved dye along with approved cameras to add more information regarding tissue characteristics then is available by normal viewing alone. It has already been shown to be associated with an improvement in safety related to healing after colorectal surgery and the investigators are sooning in a randomised trial examining this in rectal cancer to prove it. Whether or not this trial proves this or not, the ability to better understand tissue health during investigation/operation needs further examination and development. In this study, the investigators will examine the role of computer vision and machine learning in determining the nature of the tissue being seen in real-time additive to the surgeons' own opinion and experience. This is needed because the dynamic phases of fluorescence inflow into any tissue is difficult to interpret most especially when it relates to microvasculature as is present within a cancer site or deposit. By this means the investigators hope to better understand the dynamic perfusion in and out of tissue whether normal or abnormal and define signatures that can speed up and/or help inform the surgeon regarding the actual nature of the tissue being seen. The investigators will compare the data being generated with that already being captured with regard to standard pathology and radiology and other laboratory measures of clinical course. Tissue resected from a patient will also be examined in the laboratory under near-infrared microscopy and analysed for fluorescence intensity to understand where exactly and how much of the dye accumulates in specific regions of tissue. There are no new operations in this study and no new interventions are being made on the basis of the information being gathered- it's a comparative study to see how this added information can add value to interventionalists during surgery. There are four collaborating groups involved in this research consortium, two are commercial partners as they add value in both this advanced field of analytics and in the ensuring a clinical business case is included so that findings of this work can be useful for patients.
Detailed description
This is a combined retrospective and prospective, unblinded, non-CTIMP, multicentre, observational study to develop and determine methods of applying CV and AI with IFA in surgery for clinical benefit in surgery. Surgery can be performed via a minimally invasive fashion whether by an endoscopic or a laparoscopic or robotic technique (the latter depending on surgeon's preference) as part of either a diagnostic or therapeutic intervention in the standard way based on the patients' clinical need. Either before or during the procedure, a visual contrast agent will be administered by peripheral cannula and the area of interest examined by use of a near-infrared scope to determine presence, persistence and inflow/outflow pathways of the dye. The video image will be subjected to further analysis by computer vision and data analytics for the purposes of elucidating specific patterns enabling machine learning to build algorithms for flow characterisation informed by biophysics and pseudo-anonymised clinical data. The developmental algorithms will be additionally informed by mechanistic work quantifying and localising the fluorescence agent within and around sites of abnormal disease by digital fluorescence scanning and near-infrared microscopy as well as deep characterisation of dye clearance dynamics and local tissue metabolites (particularly acidosis). In addition, some tissue from the resected specimens provided in the course of diagnostic investigation or cancer surgery will be used to develop organoids for the purpose of examining in vitro tumour uptake and distribution of fluorescence agents.
In all 250 patients will be studied over the three-year period, comprising 100 patients undergoing anastomotic construction and 100 undergoing cancer diagnostics/resection. Some patients can be included in both groups). Following development (potentially earlier then above), prospective validation will be performed on approximately 25 patients in each group. The follow-up period ends 30 days after recruitment. The trial will not be blinded to participants, medical staff, or clinical trial staff. The contrast agents used are clinically approved (including indocyanine green) for such use within this study. While the validation component of this work will be performed prospectively, the initial model development will include some data from patients retrospectively who have already undergone similar evaluation.
Study design
- Observational model
- Cohort
- Time perspective
- Other
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Participants with or suspected of colorectal neoplasia undergoing endoscopic evaluation or laparoscopic resection of same or other colorectal disease by segmental resection and anastomosis. Clinically fit for elective intervention and meeting inclusion/exclusion criteria.
Key inclusion criteria (8 listed)
- • Participant is willing and able to give informed consent for participation in the study.
Key exclusion criteria (3 listed)
- • Female participant who is pregnant, lactating or planning pregnancy during the course of the study.
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 |
|---|---|---|
| Video recordings with analysis thereafter appliedRecordings of surgical assessments in order to develop a model for Artificial Intelligence analysis | Other | – |
| Examination of microsections of tissue excised for the purposes of cancer resectionMicroscopic examination including by near-infrared examination of some of the tissue excised for the purposes of cancer resection to examine cellular mechanisms of fluorescence uptake and distribution | Other | – |
| Cancer organoid development and testingSome tissue will be taken from the specimens excised for the purposes of the cancer operation to develop experimental models of cancer tissue to allow examination of mechanisms of uptake and distribution of the fluorophores in cancer | Other | – |
Primary outcome
Video recordings of Colorectal Cancer.
Time frame: 6 months
Video from colorectal endoscopies and laparoscopies recorded from patients undergoing endoscopic or laparoscopic evaluation of colorectal cancer including at the time of intravenous administration of a fluorophore (indocyanine green).
Analysis of video recordings
Time frame: 6 months
Computer vision analysis of fluorescence intensity patterns seen in the videos- i.e. ICG perfusion patterns (including presence, persistence and flow).
Biophysics visualisation software development
Time frame: 6 months
Biophysics-based visualisation software development that automatically determines ICG perfusion patterns within the field of view of the video related to different colorectal tissue types (cancer and non-cancer).
Biophysics model training
Time frame: 6 months
Results from standard clinical tests including histopathology reports used to inform the software analysis profiles so that specific, significant characterisation signatures reflective of underlying tissue architecture and behaviour (and hence nature) are generated.
Validation of predictive accuracy biophysics-visualisation model
Time frame: 3 years
Determinative analysis of accuracy of the biophysics model in the prediction of patient course including standard clinical tests (specficially histopathology and expert opinion). Calculation of accuracy, predictive values (positive and negative) and sensitivity and specificity calculation.
3 secondary outcomes
Coefficients of Variation
Time frame: 3 years
Microscopic Map of intratumoral fluorophore accumulation
Time frame: 3 years
Realtime delineation display of tumour area including margins
Time frame: 3 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Jan 2020.
- Derived from this study: PubMed 41495165Boland PA, MacAonghusa P, Singaravelu A, McEntee PD, Cucek J, Erzen S, Aigner F, Arezzo A, Burke JP, Hompes R, Tuynman JB, Neary PM, Cahill RA. Artificial intelligence classification of rectal neoplasia by endoscopic fluorescence perfusion analysis. Sci Rep. 2026 Jan 6;16(1):4761. doi: 10.1038/s41598-026-35233-x.
- Derived from this study: PubMed 36894810Hardy NP, MacAonghusa P, Dalli J, Gallagher G, Epperlein JP, Shields C, Mulsow J, Rogers AC, Brannigan AE, Conneely JB, Neary PM, Cahill RA. Clinical application of machine learning and computer vision to indocyanine green quantification for dynamic intraoperative tissue characterisation: how to do it. Surg Endosc. 2023 Aug;37(8):6361-6370. doi: 10.1007/s00464-023-09963-2. Epub 2023 Mar 9.
- Individual participant dataSharing planned: No
Related trials for Colorectal Cancer
Not yet recruitingPhase 1
Hepatic Artery Infusion of Carfilzomib in Participants With Liver Metastatic Disease Previously Treated With Hepatic Artery Infusion Pump Therapy
Colorectal Neoplasms, Neoplasms
Active, not recruiting
Streamlined Geriatric and Oncological Evaluation Based On IC Technology
Breast Cancer, Comorbidities and Coexisting Conditions
RecruitingObservational study
Pre- and On-treatment Multi-omics Profiling of Locally Advanced Rectal Cancer
Rectal Neoplasms
Enrolling by invitationObservational study
Natural History Study of Early Life Exposures in Agriculture (ELEA)
Prostate Cancer, Leukemia
RecruitingPhase 2
Tislelizumab in People With Colorectal Cancer
Colorectal Cancer
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. This study is not recruiting at the moment.
Ronan Cahill principal investigator
University College Dublin
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 Colorectal Cancer and Surgery, run by Mater Misericordiae University Hospital at 1 site in Ireland.
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 8 inclusion and 3 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: status not confirmed, as of the sponsor's last update on 7 Jan 2020. 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 Ireland. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Ronan Cahill (+35317164597, ronan.cahill@ucd.ie), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Video recordings of Colorectal Cancer. (time frame: 6 months). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04220242, which also lists 2 linked publications.
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 7 Jan 2020.