NCT04220242: FOCCuS1

Future of Colorectal Cancer Surgery

Status unknown Last updated 7 January 2020

What this trial tests

trial testing Video recordings with analysis thereafter applied in Colorectal Cancer in 250 participants. Status unknown.

Timeline

30 December 2019

Primary endpoint
1 December 2022

1 March 2023

Quick facts

Lead sponsor	Mater Misericordiae University Hospital
Status	Status unknown
Study type	OBSERVATIONAL
Enrollment	250
Start date	30 December 2019
Primary completion	1 December 2022
Estimated completion	1 March 2023
Sites	1 location across Ireland

Drugs / interventions tested

• Video recordings with analysis thereafter applied
• Examination of microsections of tissue excised for the purposes of cancer resection
• Cancer organoid development and testing

Conditions studied

• Colorectal Cancer — all drugs for Colorectal Cancer →
• Surgery — all drugs for Surgery →

Sponsor

Mater Misericordiae University Hospital

Who can join

18 and older, any sex, with Colorectal Cancer or Surgery. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

8 peer-reviewed publications reference this trial (live from Europe PMC):

1. Surgical data science - from concepts toward clinical translation.
   Maier-Hein L, Eisenmann M, Sarikaya D, März K, et al · · 2022 · cited 158× · PMID 34879287 · DOI 10.1016/j.media.2021.102306
2. Digital dynamic discrimination of primary colorectal cancer using systemic indocyanine green with near-infrared endoscopy.
   Dalli J, Loughman E, Hardy N, Sarkar A, et al · · 2021 · cited 21× · PMID 34059705 · DOI 10.1038/s41598-021-90089-7
3. Clinical application of machine learning and computer vision to indocyanine green quantification for dynamic intraoperative tissue characterisation: how to do it.
   Hardy NP, MacAonghusa P, Dalli J, Gallagher G, et al · · 2023 · cited 16× · PMID 36894810 · DOI 10.1007/s00464-023-09963-2
4. Evaluating clinical near-infrared surgical camera systems with a view to optimizing operator and computational signal analysis.
   Dalli J, Jindal A, Gallagher G, Epperlein JP, et al · · 2023 · cited 11× · PMID 37009578 · DOI 10.1117/1.jbo.28.3.035002
5. Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art.
   Boland PA, Hardy NP, Moynihan A, McEntee PD, et al · · 2024 · cited 10× · PMID 38858280 · DOI 10.1007/s00259-024-06731-9
6. Artificial intelligence for optimum tissue excision with indocyanine green fluorescence angiography for flap reconstructions: Proof of concept.
   Singaravelu A, Dalli J, Potter S, Cahill RA. · · 2024 · cited 5× · PMID 39252988 · DOI 10.1016/j.jpra.2024.07.014
7. Clinical and computational development of a patient-calibrated ICGFA bowel transection recommender.
   Dalli J, Epperlein JP, Hardy NP, Khan MF, et al · · 2024 · cited 4× · PMID 38637339 · DOI 10.1007/s00464-024-10827-6
8. Abstracts from the 47^th Sir Peter Freyer Surgical Symposium 2022.
   · 2022 · cited 3× · PMID 36471125 · DOI 10.1007/s11845-022-03228-y