Trial Title:
CLASSICA: Validating AI in Classifying Cancer in Real-Time Surgery Study 1
NCT ID:
NCT05793554
Condition:
Rectum Neoplasm
Rectum Polyp
Rectal Cancer
Conditions: Official terms:
Rectal Neoplasms
Conditions: Keywords:
Fluorescence
Indocyanine green
Tumour classification
Artificial Intelligence
Study type:
Observational
Overall status:
Not yet recruiting
Study design:
Time perspective:
Prospective
Summary:
Cancer of the lowermost part of the intestine (the rectum) is a common disease and both
this disease and its treatment can have major impact on patients. Unless treated early,
the disease can progress, spread to other parts of the body and ultimately cause death.
Treatment often involves radical surgery, but this too has consequences and risks major
complications. Best outcomes regarding cure with least impact depend on the disease being
detected at an early stage as rectal cancer tends to start first as a non-cancerous
polyp.
The smallest of these precursor polyps can be easily removed during a routine colonoscopy
but once the polyp grows over 2cm in size it is much harder to categorise correctly as
the risk of it containing cancer somewhere in it increases markedly. If there is
definitely cancer present in such a polyp it is best treated from the outset as a cancer
with major surgery, but if there is definitely not a cancer in it then it can be removed
from inside the bowel with minimally invasive techniques. Unfortunately, despite our
current very best methods, up to 20% of tumours initially thought to be benign are found
to be malignant only after they are excised
We have previously shown that cancerous and non-cancerous tissues can be visually
differentiated by analysis of their perfusion during the examination. For this we use a
specific approved fluorescent dye, indocyanine green (ICG). ICG is commonly used in bowel
surgery anyway to assess the blood supply to the bowel and has a very good safety
profile. ICG is injected into the bloodstream during surgery and the rate at which it is
taken up by various tissue types is detected by specific and approved cameras which can
reveal fluorescence in tissue. We have previously found that the rate of uptake of this
dye is different in cancer tissue compared to non-cancer tissue and have used artificial
intelligence algorithms to measure this difference. However, we now need to ensure that
this method can work also in other patients, in other centres and indeed in other
countries to ensure it is indeed a valid and useful way of assessing rectal polyps.
The goal of this observational study is to validate the use of fluorescence pattern
analysis in the classification of rectal tumours. Patients enrolled in the study will
attend for a visual examination of the rectal tumour in theatre as is standard practice.
During this examination a video recording of the fluorescence perfusion will be taken
following ICG administration. Patients will then have the tumour excised or treated as is
standard of care by their surgeon. The video will later be analysed to determine the
pattern of fluorescence perfusion within the tumour, and a classification will be
assigned based on the pattern seen. All tumours that are excised are examined under the
microscope by a pathologist to determine the final diagnosis. The fluorescence based
classification will be compared to this pathological diagnosis to determine the accuracy
of the method. So, patients will still have the exact same standard of care as currently
happens, the hope is that in future this method can be developed to the point where it
could be useful by means of a useable, accurate automated software process. If so, that
will form the basis of another study in the future to look to see if it can guide or even
replace biopsies and help with ensuring complete removal ('clear margins') after
excision.
Detailed description:
Rectal polyps >2cm in size (affecting c. 10,000 patients a year in Europe alone)
represent a considerable clinical challenge. While smaller polyps can be addressed by
routine endoscopic polypectomy and frank clinical cancer will advance through a
traditional cancer surgery paradigm, polyps of this size have the option of being locally
excised, intact by transanal endoscopic resection (also referred to as Transanal
Minimally Invasive Surgery, TAMIS). This is the treatment of choice in this site due to
its ability to provide a single complete unfragmented specimen versus other modalities
(e.g. endoscopic submucosal resection). While the technology and training to enable
transanal resection has become much more available over the past decade (especially
TAMIS) meaning more patients can have large benign lesions and even some early rectal
cancers excised in their local specialist centres, the major brake now on such care is
patient selection: i.e. how to tell if a given patient has a benign polyp or a cancer in
advance of its resection.
Endoscopic biopsies are notoriously inaccurate in up to 20% of such lesions (rectal
cancers commence most often as adenomatous lesions and so superficial biopsies may miss a
malignant focus). Mistakenly identifying a cancer as a benign lesion and treating it by
local excision significantly worsens prognosis and compromises subsequent cancer surgery
- including potentially converting a reconstructable site of resection (i.e. a lesion
suitable for anterior resection) to an unreconstructable one (i.e. needing an
abdominoperineal resection with permanent colostomy) and by seeding cancer cells into a
deep margin or different plane, particularly as in the case for anteriorly positioned
lesions. Additionally, transanal excision techniques continue to have relatively high
rates of positive margins; this risks regrowth in benign lesions and limits effective
local therapy for earliest stage cancers due to the presence of inapparent disease close
to the main tumour bulk.
We have previously demonstrated, through the use of fluorescent indocyanine green (ICG),
that perfusion is visibly different, between tumour and healthy tissue. This difference
can be captured via infrared video and mathematical analysis can differentiate the
perfusion pattern of malignant areas from any benign/normal tissue also visible in the
same endoscopic view. In brief, the saturation of fluorescence in each region of interest
(ie tumour or area of normal mucosa), can be measured from the recorded video using
existing software developed by IBM. The change in fluorescence over time can be plotted
on a curve, demonstrating the inflow, peak and outflow of ICG, which is depending on the
perfusion patters within the region of interest. These curves differ depending on the
tissue being examined and so can be used to classify benign from malignant tumours
through calculating the slope of the uptake and area under the curve to measure outflow.
Therefore, in a location (such as the rectum) where a cancer is suspected, analysis of
the video can be used to differentiate between healthy and cancerous tissues. This
discovery can be made exploited for clinical use by the application of AI methods
including computer vision and machine learning. In essence, the fluorescence intensity of
pixels displaying tissues of interest varies with blood flow (perfusion), when the blood
is dyed with ICG and lit by near-infra-red (NIR) light. The intensity is captured over
time, from multiple video frames, and this intensity is plotted as a curve. The intensity
curves of tumour tissue are different from those of healthy tissue, and those of benign
tumours are different from malignant tumours. Analysis of the curve features for each
pixel in a region of interest can thus lead to a classification.
Such an AI system has been prototyped and trained in the Mater Hospital previously with
videos from a population of Irish cancer patients from two regional centres, so that it
can automatically identify malignant tumours and benign lesions from healthy tissue by
their perfusion patterns. This prototype has previously demonstrated accuracy of >80%.
In this study, we clinically validate the basic concept or method of classifying tissue
by its fluorescence signal characteristics while also seeing if a device can be built on
the basis of this that can extrapolate the data being generated from the videos by UCD
staff. We also address the question of generalisability - can other surgeons use the
system and get similar results from their specific patient cohorts? This will pave the
way for future studies which are planned to determine the roles of biopsy (can the system
enable optimal choice of biopsied tissue, and thus reduce biopsy error?); and tumour
resection (can the system increase the completeness and accuracy of tumour resection?).
Criteria for eligibility:
Study pop:
Adult patients fitting the inclusion criteria will be approached for inclusion in the
trial. Participants must be able and willing to comply with the terms of the protocol.
Such patients will be identified from referral letters, outpatient clinics, endoscopy
lists, and multidisciplinary cancer meetings. Patients will undergo standard preoperative
work-up, including but not limited to colonic visualisation by either colonoscopy or CT
colonogram, staging CT scan of the chest, abdomen and pelvis, MRI of the rectum, and
assessment of fitness for surgery as per standard practice. The optimal management of the
patient will be determined based on institutional protocols.
Suitability for inclusion as per the selection criteria above will be assessed and
patients will be provided with verbal and written details.
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
- Participants with a confirmed or suspected rectal polyp/tumour measuring greater
than 2cm undergoing surgical intervention or assessment OR Patients with a known
rectal cancer undergoing surgical intervention or assessment, including those post
neo adjuvant therapy.
- Participant is willing and able to give informed consent for participation in the
study. ● Male or Female, aged 18 years or above.
- Clinically fit for elective intervention
Exclusion Criteria:
- Female participant who is pregnant, lactating or planning pregnancy within three
months 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. ● Allergy to intravenous contrast agent or iodides
- Other contraindications to ICG including concurrent use of anticonvulsants,
bisulphite containing drugs, methadone and nitrofurantoin.
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Mater Misericordiae University Hospital
Address:
City:
Dublin
Zip:
D07 R2WY
Country:
Ireland
Start date:
March 2023
Completion date:
March 2027
Lead sponsor:
Agency:
Mater Misericordiae University Hospital
Agency class:
Other
Collaborator:
Agency:
Institut de recherche Contre Les Cancers de L'appareil Digestif
Agency class:
Other
Collaborator:
Agency:
Stitchting EAES
Agency class:
Other
Collaborator:
Agency:
Pintail LTD
Agency class:
Other
Collaborator:
Agency:
Kobenhavns Universitet
Agency class:
Other
Collaborator:
Agency:
Universita Degli Studi di Torino
Agency class:
Other
Collaborator:
Agency:
Ziekenhuis Oost-Limburg Autonome Verzorginginstelling
Agency class:
Other
Collaborator:
Agency:
Arctur Racunalniski Inzeniring Doo
Agency class:
Other
Collaborator:
Agency:
Stitchting VUMC
Agency class:
Other
Collaborator:
Agency:
Penn State University
Agency class:
Other
Collaborator:
Agency:
Krankenhaus der Barmherzigen Bruder Graz
Agency class:
Other
Collaborator:
Agency:
Horizon Europe
Agency class:
Other
Source:
Mater Misericordiae University Hospital
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05793554
https://classicaproject.eu/
