Clinical Validation of PolyDeep: an Artificial Intelligence-based Computer-aided Polyp Detection (CADe) and Characterization (CADx) System. Polydeep Advance 2.

Colorectal Cancer Clinical Trial

Official title:

PolyDeep Advance 2: Clinical Validation of PolyDeep. Randomized Clinical Trial With a Tandem Colonoscopy Design.

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05512793
• Other study ID #: PolyDeep Advance 2.0
• Status: Completed
• Phase: N/A
• Start date: May 24, 2023
• Est. completion date: November 17, 2023

Study information

• Verified date: November 2023
• Source: Fundacin Biomedica Galicia Sur
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

This study is a clinical validation of PolyDeep, a computer-aided polyp detection (CADe) and characterization (CADx) system. PolyDeep Advance 2 is a multicentric randomized clinical trial with a tandem colonoscopy design. The hypothesis of this study is that Polydeep assisted colonoscopy will reduce the number of missed adenomas in the first withdrawal.

Description:

Colorectal cancer (CRC) is the most frequently cancer in western world. A fundamental tool for detection and prevention is the colonoscopy. The detection and endoscopic resection of colorectal polyps, the precursor lesion of CRC, can reduce CRC incidence and mortality. Adenoma detection rate is the most used endoscopic quality indicator. The improvement of this indicator is related to the reduction of postcolonoscopy CRC incidence and mortality.

Colorectal polyp diagnosis is based on endoscopic resection and histological analysis. An accurate optical diagnosis could avoid histological lesion of smaller lesions, reducing the costs associated with histological diagnosis. The NICE (NBI International Colorectal Endoscopic) Classification has proposed the use of high definition endoscopes that have Narrow Band Imaging. However, NICE must be used by endoscopists who are sufficiently prepared and who have overcome the learning curve. Therefore, optical histology diagnosis with high accuracy independently of the center and the endoscopist is necessary.

Computer Aid Diagnosis (CAD) systems based on Artificial Intelligence are experiencing exponential development in the field of medical image analysis. The development of the CAD system is based on the creation of large databases of endoscopic images and/or videos, on the training, development and validation of diagnostic algorithms in such databases and, finally, on prospective clinical validation in patients undergoing colonoscopy. The goal of CAD systems in colonoscopy is double. First, it aims to increase the detection of polyps (CADe) in general, and of adenomas and serrated lesions in particular. The second objective is to characterize (CADx) the histology of detected lesion.

PolyDeep CAD is a functional prototype. It is capable of detecting, locating and classifying colorectal polyps. In vivo validation data shows that PolyDeep has high diagnostic accuracy for polyp identification and that this accuracy can be accommodated This clinical trial is part of the clinical validation of PolyDeep. We will perform a randomized clinical trial with a tandem colonoscopy design with adenoma miss rate as the main objective.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 260
• Est. completion date: November 17, 2023
• Est. primary completion date: July 30, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 40 Years to 79 Years

Eligibility

Inclusion Criteria:

• First diagnostic colonoscopy performed after a positive fecal immunochemical test performed within the CRC screening program.

• Surveillance after colorectal adenomas resection.

• Authorization after reading the information sheet and singing the informed consent.

Exclusion Criteria:

• Incomplete colonoscopy without cecal intubation.

• Colonoscopies with insufficient intestinal cleansing (Boston Bowel Preparation Scale <6 or <2 in any of the evaluated segments).

• Detected lesions without histological diagnosis.

• Previous CRC.

• Previous colonic resection.

• CRC associated hereditary syndrome.

• Serrated polyposis syndrome.

Related Conditions & MeSH terms

• Colorectal Cancer
• Colorectal Neoplasms

Intervention

Diagnostic Test:
• Standard technique First colonoscopy without PolyDeep
Standard technique First colonoscopy without PolyDeep
• Combination technique First colonoscopy with PolyDeep
Combination technique First colonoscopy with PolyDeep

Locations

Country	Name	City	State
Spain	Complexo Hospitalario Universitario de Ourense	Ourense
Spain	Complexo Hospitalario Universitario de Pontevedra	Pontevedra
Spain	Hospital Álvaro Cunqueiro	Vigo

Sponsors (1)

Lead Sponsor	Collaborator
Fundacin Biomedica Galicia Sur

Country where clinical trial is conducted

Spain, 

References & Publications (11)

ASGE Technology Committee; Abu Dayyeh BK, Thosani N, Konda V, Wallace MB, Rex DK, Chauhan SS, Hwang JH, Komanduri S, Manfredi M, Maple JT, Murad FM, Siddiqui UD, Banerjee S. ASGE Technology Committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc. 2015 Mar;81(3):502.e1-502.e16. doi: 10.1016/j.gie.2014.12.022. Epub 2015 Jan 16. — View Citation

Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, Zauber AG, de Boer J, Fireman BH, Schottinger JE, Quinn VP, Ghai NR, Levin TR, Quesenberry CP. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014 Apr 3;370(14):1298-306. doi: 10.1056/NEJMoa1309086. — View Citation

Cubiella J, Marzo-Castillejo M, Mascort-Roca JJ, Amador-Romero FJ, Bellas-Beceiro B, Clofent-Vilaplana J, Carballal S, Ferrandiz-Santos J, Gimeno-Garcia AZ, Jover R, Mangas-Sanjuan C, Moreira L, Pellise M, Quintero E, Rodriguez-Camacho E, Vega-Villaamil P; Sociedad Espanola de Medicina de Familia y Comunitaria y Asociacion Espanola de Gastroenterologia. Clinical practice guideline. Diagnosis and prevention of colorectal cancer. 2018 Update. Gastroenterol Hepatol. 2018 Nov;41(9):585-596. doi: 10.1016/j.gastrohep.2018.07.012. Epub 2018 Sep 20. English, Spanish. — View Citation

Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, Antonelli G, Yu H, Areia M, Dinis-Ribeiro M, Bhandari P, Sharma P, Rex DK, Rosch T, Wallace M, Repici A. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021 Jan;93(1):77-85.e6. doi: 10.1016/j.gie.2020.06.059. Epub 2020 Jun 26. — View Citation

Jin EH, Lee D, Bae JH, Kang HY, Kwak MS, Seo JY, Yang JI, Yang SY, Lim SH, Yim JY, Lim JH, Chung GE, Chung SJ, Choi JM, Han YM, Kang SJ, Lee J, Chan Kim H, Kim JS. Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations. Gastroenterology. 2020 Jun;158(8):2169-2179.e8. doi: 10.1053/j.gastro.2020.02.036. Epub 2020 Feb 29. — View Citation

Mangas-Sanjuan C, Santana E, Cubiella J, Rodriguez-Camacho E, Seoane A, Alvarez-Gonzalez MA, Suarez A, Alvarez-Garcia V, Gonzalez N, Lue A, Cid-Gomez L, Ponce M, Bujanda L, Portillo I, Pellise M, Diez-Redondo P, Herraiz M, Ono A, Pizarro A, Zapater P, Jover R; QUALISCOPIA Study Investigators. Variation in Colonoscopy Performance Measures According to Procedure Indication. Clin Gastroenterol Hepatol. 2020 May;18(5):1216-1223.e2. doi: 10.1016/j.cgh.2019.08.035. Epub 2019 Aug 22. — View Citation

Parmar R, Martel M, Rostom A, Barkun AN. Validated Scales for Colon Cleansing: A Systematic Review. Am J Gastroenterol. 2016 Feb;111(2):197-204; quiz 205. doi: 10.1038/ajg.2015.417. Epub 2016 Jan 19. — View Citation

Parsa N, Rex DK, Byrne MF. Colorectal polyp characterization with standard endoscopy: Will Artificial Intelligence succeed where human eyes failed? Best Pract Res Clin Gastroenterol. 2021 Jun-Aug;52-53:101736. doi: 10.1016/j.bpg.2021.101736. Epub 2021 Feb 22. — View Citation

Puig I, Lopez-Ceron M, Arnau A, Rosinol O, Cuatrecasas M, Herreros-de-Tejada A, Ferrandez A, Serra-Burriel M, Nogales O, Vida F, de Castro L, Lopez-Vicente J, Vega P, Alvarez-Gonzalez MA, Gonzalez-Santiago J, Hernandez-Conde M, Diez-Redondo P, Rivero-Sanchez L, Gimeno-Garcia AZ, Burgos A, Garcia-Alonso FJ, Bustamante-Balen M, Martinez-Bauer E, Penas B, Pellise M; EndoCAR group, Spanish Gastroenterological Association and the Spanish Digestive Endoscopy Society. Accuracy of the Narrow-Band Imaging International Colorectal Endoscopic Classification System in Identification of Deep Invasion in Colorectal Polyps. Gastroenterology. 2019 Jan;156(1):75-87. doi: 10.1053/j.gastro.2018.10.004. Epub 2018 Oct 6. — View Citation

Wani S, Rastogi A. Narrow-band imaging in the prediction of submucosal invasive colon cancer: how "NICE" is it? Gastrointest Endosc. 2013 Oct;78(4):633-6. doi: 10.1016/j.gie.2013.06.015. No abstract available. — View Citation

Zhao S, Wang S, Pan P, Xia T, Chang X, Yang X, Guo L, Meng Q, Yang F, Qian W, Xu Z, Wang Y, Wang Z, Gu L, Wang R, Jia F, Yao J, Li Z, Bai Y. Magnitude, Risk Factors, and Factors Associated With Adenoma Miss Rate of Tandem Colonoscopy: A Systematic Review and Meta-analysis. Gastroenterology. 2019 May;156(6):1661-1674.e11. doi: 10.1053/j.gastro.2019.01.260. Epub 2019 Feb 6. — View Citation

* Note: There are 11 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Adenoma miss rate (AMR)	AMR will be calculated as the number of adenomas detected on the withdrawal or portion in either group divided by the total number of adenomas detected during both withdrawals	1 Year
Secondary	Polyp miss rate	It will be calculated as the number of polyps detected on the withdrawal or portion in either group divided by the total number of polyps detected during both withdrawals	1 Year
Secondary	Serrated lesions miss rate	It will be calculated as the number of serrated lesions detected on the withdrawal or portion in either group divided by the total number of serrated lesions detected during both withdrawals	1 Year
Secondary	Diagnostic yield and lesions characterization	The diagnostic yield will be measure using sensitivity , specificity, Positive Predictive Value, Negative Predictive Value, likelihood ratios and Youden index. Histological diagnosis will be used as the gold standard.	1 Year
Secondary	Withdrawal time. (First/second colonoscopy)	Withdrawal time between the two arms will be calculated and compared.	1 Year

External Links

•   Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.
•   Polydeep website