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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT05034185
Other study ID # CIRB Ref: 2021/2001
Secondary ID
Status Completed
Phase
First received
Last updated
Start date March 3, 2021
Est. completion date October 1, 2022

Study information

Verified date February 2023
Source Changi General Hospital
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide, with rates of CRC predicted to increase. Colonoscopy is currently the gold standard of screening for CRC. Artificial intelligence (AI) is seen as a solution to bridge this gap in adenoma detection, which is a quality indicator in colonoscopy. AI systems utilize deep neural networks to enable computer-aided detection (CADe) and computer-aided classification (CADx). CADe is concerned with the detection of polyps during colonoscopy, which in turn is postulated to help decrease the adenoma miss-rate. In contrast, CADx deals with the interpretation of polyp appearance during colonoscopy to determine the predicted histology. Prediction of polyp histology is crucial in helping Clinicians decide on a "resect and discard" or "diagnose and leave strategy". It is also useful for the Clinician to be aware of the predicted histology of a colorectal polyp in determining the appropriate method of resection in terms of safety and efficacy. While CADe has been studied extensively in randomized controlled trials, there is a lack of prospective data validating the use of CADx in a clinical setting to predict polyp histology. The investigators plan to conduct a prospective, multi-centre clinical trial to validate the accuracy of CADx support for prediction of polyp histology in real-time colonoscopy.


Description:

Colonoscopy is currently the gold standard of screening for CRC. A 1% increase in adenoma detection rate (ADR) estimated to be associated with a 3% decreased risk of interval CRC. AI systems can be broadly divided into CADe (for detection) and CADx (for diagnosis, or prediction of polyp histology in the context of colonoscopy). CADe has been extensively studied, with several randomized controlled trials and meta-analysis showing a higher ADR when CADe is used compared to the control groups without CADe. Besides the ADR, predicted polyp histology is a key component in the performance of colonoscopy as this enables the Clinician to make a decision regarding its management, as described above. In this regards, image-enhanced endoscopy (IEE) is often used to help Clinicians determine if colorectal polyps found on colonoscopy are neoplastic or hyperplastic. The most commonly used non-magnification classification is the NBI International Colorectal Endoscopic (NICE), while the Japan NBI Expert Team (JNET) classification is used where endoscopy systems with optical magnification and the proper training is available. However, these classification systems have varying diagnostic accuracy and interobserver agreement. Previous prospective studies looking at CADx have utilized endocytoscopy and autofluorescence imaging (CAD-AFI) with positive results. However, the major limitation in these CADx studies is that these imaging systems are costly and are not readily available in most centres worldwide. Furthermore, most Clinicians performing colonoscopies have not been trained in these modalities of imaging and will have to rely completely on the CADx function to detect polyps if these imaging modalities are used, without being able to fall back on their experience and training should there be doubts about the accuracy of a CADx diagnosis in a real-world setting. The Fujifilm 7000 System (Fujifilm Corp., Tokyo) has been in routine clinical use in all tertiary institutions in Singapore. The CAD EYE system was developed by Fujifilm Corp to aid Clinicians in colonoscopy with CADe and CADx functions. The basic functions and handling of the colonoscope, as well as the endoscopy processing unit, are similar to what is currently available in clinical practice, with the added CAD EYE software. The controller has been configured to allow the operator to activate and deactivate the CAD function depending on the need for it. These functions can be turned on and off using a button on the controller by the Clinician. The CADe and CADx functions operate when white light and blue laser imaging (BLI) are used, respectively. This provides a unique opportunity to externally validate the use of the CADx support tool by evaluating its diagnostic accuracy with final polyp histology as the gold standard, while also comparing its performance in a clinical setting against a Clinician using IEE (which is the conventional method of predicting polyp histology in colonoscopy). The investigators plan to conduct a prospective, multi-centre clinical trial to validate the accuracy of CADx support for prediction of polyp histology in real-time colonoscopy.


Recruitment information / eligibility

Status Completed
Enrollment 450
Est. completion date October 1, 2022
Est. primary completion date July 31, 2022
Accepts healthy volunteers
Gender All
Age group 40 Years and older
Eligibility Inclusion Criteria: 1. Patients who have an indication for colonoscopy and who have at least one polyp detected during colonoscopy 2. 40 years of age and above 3. Consent obtained for the study Exclusion Criteria: 1. Less than 39 years of age 2. Declined participation in study 3. Patients with no polyps detected during colonoscopy 4. Patients with inflammatory bowel disease 5. Patients with known unresected colorectal cancer

Study Design


Intervention

Device:
Computer-aided diagnosis (CADx) support tool
The CADx support tool operates when the Clinician switches the preconfigured CAD EYE function on using a button on the controller while the scope system is in BLI mode. This is performed after the Clinician first makes an optical prediction of polyp histology using IEE as described. The CADx support tool will make a prediction of polyp histology as "hyperplastic" or "neoplastic".

Locations

Country Name City State
Singapore Changi General Hospital, National University Hospital, Singapore General Hospital and Tan Tock Seng Hospital Singapore

Sponsors (4)

Lead Sponsor Collaborator
Changi General Hospital National University Hospital, Singapore, Singapore General Hospital, Tan Tock Seng Hospital

Country where clinical trial is conducted

Singapore, 

References & Publications (27)

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Horiuchi H, Tamai N, Kamba S, Inomata H, Ohya TR, Sumiyama K. Real-time computer-aided diagnosis of diminutive rectosigmoid polyps using an auto-fluorescence imaging system and novel color intensity analysis software. Scand J Gastroenterol. 2019 Jun;54(6):800-805. doi: 10.1080/00365521.2019.1627407. Epub 2019 Jun 14. — View Citation

Kaminski MF, Thomas-Gibson S, Bugajski M, Bretthauer M, Rees CJ, Dekker E, Hoff G, Jover R, Suchanek S, Ferlitsch M, Anderson J, Roesch T, Hultcranz R, Racz I, Kuipers EJ, Garborg K, East JE, Rupinski M, Seip B, Bennett C, Senore C, Minozzi S, Bisschops R, Domagk D, Valori R, Spada C, Hassan C, Dinis-Ribeiro M, Rutter MD. Performance measures for lower gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. Endoscopy. 2017 Apr;49(4):378-397. doi: 10.1055/s-0043-103411. Epub 2017 Mar 7. — View Citation

Kandel P, Wallace MB. Should We Resect and Discard Low Risk Diminutive Colon Polyps. Clin Endosc. 2019 May;52(3):239-246. doi: 10.5946/ce.2018.136. Epub 2019 Jan 21. — View Citation

Komeda Y, Kashida H, Sakurai T, Asakuma Y, Tribonias G, Nagai T, Kono M, Minaga K, Takenaka M, Arizumi T, Hagiwara S, Matsui S, Watanabe T, Nishida N, Chikugo T, Chiba Y, Kudo M. Magnifying Narrow Band Imaging (NBI) for the Diagnosis of Localized Colorectal Lesions Using the Japan NBI Expert Team (JNET) Classification. Oncology. 2017;93 Suppl 1:49-54. doi: 10.1159/000481230. Epub 2017 Dec 20. — View Citation

Mori Y, Kudo SE, Misawa M, Saito Y, Ikematsu H, Hotta K, Ohtsuka K, Urushibara F, Kataoka S, Ogawa Y, Maeda Y, Takeda K, Nakamura H, Ichimasa K, Kudo T, Hayashi T, Wakamura K, Ishida F, Inoue H, Itoh H, Oda M, Mori K. Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study. Ann Intern Med. 2018 Sep 18;169(6):357-366. doi: 10.7326/M18-0249. Epub 2018 Aug 14. — View Citation

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Repici A, Badalamenti M, Maselli R, Correale L, Radaelli F, Rondonotti E, Ferrara E, Spadaccini M, Alkandari A, Fugazza A, Anderloni A, Galtieri PA, Pellegatta G, Carrara S, Di Leo M, Craviotto V, Lamonaca L, Lorenzetti R, Andrealli A, Antonelli G, Wallace M, Sharma P, Rosch T, Hassan C. Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial. Gastroenterology. 2020 Aug;159(2):512-520.e7. doi: 10.1053/j.gastro.2020.04.062. Epub 2020 May 1. — View Citation

Repici A, Ciscato C, Correale L, Bisschops R, Bhandari P, Dekker E, Pech O, Radaelli F, Hassan C. Narrow-band Imaging International Colorectal Endoscopic Classification to predict polyp histology: REDEFINE study (with videos). Gastrointest Endosc. 2016 Sep;84(3):479-486.e3. doi: 10.1016/j.gie.2016.02.020. Epub 2016 Feb 27. — View Citation

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Rex DK, Schoenfeld PS, Cohen J, Pike IM, Adler DG, Fennerty MB, Lieb JG 2nd, Park WG, Rizk MK, Sawhney MS, Shaheen NJ, Wani S, Weinberg DS. Quality indicators for colonoscopy. Gastrointest Endosc. 2015 Jan;81(1):31-53. doi: 10.1016/j.gie.2014.07.058. Epub 2014 Dec 2. No abstract available. — View Citation

Song EM, Park B, Ha CA, Hwang SW, Park SH, Yang DH, Ye BD, Myung SJ, Yang SK, Kim N, Byeon JS. Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model. Sci Rep. 2020 Jan 8;10(1):30. doi: 10.1038/s41598-019-56697-0. — View Citation

Su JR, Li Z, Shao XJ, Ji CR, Ji R, Zhou RC, Li GC, Liu GQ, He YS, Zuo XL, Li YQ. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc. 2020 Feb;91(2):415-424.e4. doi: 10.1016/j.gie.2019.08.026. Epub 2019 Aug 24. — View Citation

van Rijn JC, Reitsma JB, Stoker J, Bossuyt PM, van Deventer SJ, Dekker E. Polyp miss rate determined by tandem colonoscopy: a systematic review. Am J Gastroenterol. 2006 Feb;101(2):343-50. doi: 10.1111/j.1572-0241.2006.00390.x. — View Citation

Vinsard DG, Mori Y, Misawa M, Kudo SE, Rastogi A, Bagci U, Rex DK, Wallace MB. Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointest Endosc. 2019 Jul;90(1):55-63. doi: 10.1016/j.gie.2019.03.019. Epub 2019 Mar 26. — View Citation

von Renteln D, Kaltenbach T, Rastogi A, Anderson JC, Rosch T, Soetikno R, Pohl H. Simplifying Resect and Discard Strategies for Real-Time Assessment of Diminutive Colorectal Polyps. Clin Gastroenterol Hepatol. 2018 May;16(5):706-714. doi: 10.1016/j.cgh.2017.11.036. Epub 2017 Nov 23. — View Citation

Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, Liu P, Li L, Song Y, Zhang D, Li Y, Xu G, Tu M, Liu X. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019 Oct;68(10):1813-1819. doi: 10.1136/gutjnl-2018-317500. Epub 2019 Feb 27. — View Citation

Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, Lei L, Li L, Guo Z, Lei S, Xiong F, Wang H, Song Y, Pan Y, Zhou G. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020 Apr;5(4):343-351. doi: 10.1016/S2468-1253(19)30411-X. Epub 2020 Jan 22. Erratum In: Lancet Gastroenterol Hepatol. 2020 Apr;5(4):e3. — View Citation

* Note: There are 27 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Primary To evaluate the diagnostic performance of the CADx support tool compared to optical prediction of polyp histology by the Clinician in real-time colonoscopy in a clinical setting Polyp histology used as gold standard 1 year
Secondary To determine the diagnostic performance of CADx versus optical prediction of polyp histology by endoscopist in the subgroup analysis Subgroups include bowel preparation, size of polyp and location 1 year
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