Evaluation of the Diagnostic Potential of Artificial Intelligence-assisted Fecal Microbiome Testing for Colon Cancer

Colon Cancer Clinical Trial

Official title:

Comparison of the Diagnostic Potential of Colonoscopy, and Artificial Intelligence-assisted Fecal Microbiome Testing for Colon Cancer

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05795725
• Other study ID #: 2022-12-07
• Status: Recruiting
• Phase: N/A
• Start date: May 1, 2023
• Est. completion date: May 31, 2024

Study information

• Verified date: February 2023
• Source: Istanbul Medipol University Hospital
• Contact: Varol TUNALI, Dr.
• Phone: 00905556303231
• Email: varoltunali[@]gmail.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The goal of this clinical trial is to evaluate the diagnostic potential of Artificial Intelligence-assisted Fecal Microbiome Testing for the diagnosis of colon cancer. The main question it aims to answer is:

• Is Artificial Intelligence-assisted Fecal Microbiome Testing a reliable screening test for colon cancer?

Participants will be asked to provide fecal samples to be analyzed with next-generation sequencing techniques.

If there is a comparison group: Researchers will compare the diagnostic performance of AI-assisted Fecal Microbiome Testing with colonoscopy to see the correlation between the results of both interventions.

Description:

Colon cancer, also known as colorectal cancer, is the third most commonly diagnosed cancer worldwide and the second leading cause of cancer deaths. In the United States alone, it is estimated that there will be approximately 149,500 new cases and 52,980 deaths from colorectal cancer in 2021. However, if detected early, it is highly treatable and curable.

Currently, the gold standard for colon cancer screening is a colonoscopy, which involves the insertion of a flexible tube with a camera into the rectum to examine the colon for signs of cancer or precancerous growths called polyps. While effective, this procedure is invasive, uncomfortable, and can be costly. As a result, many people delay or avoid colon cancer screening, which can lead to delayed detection and worse outcomes.

Fecal microbiome testing is a promising alternative to colonoscopy as a screening tool for colon cancer. The human gut is home to trillions of bacteria that play a critical role in maintaining our health, and research has shown that changes in the gut microbiome can be associated with the development of colon cancer. Artificial Intelligence-assisted fecal microbiome testing involves analyzing the composition of the gut microbiome using advanced algorithms and machine learning techniques to identify patterns that are indicative of colon cancer.

This non-invasive, low-cost, and convenient screening test has the potential to significantly increase colon cancer screening rates and reduce the number of deaths from this disease. By identifying individuals at high risk of colon cancer at an early stage, Artificial Intelligence-assisted fecal microbiome testing can lead to earlier intervention and better outcomes. Therefore, the diagnostic potential of AI-assisted fecal microbiome testing for colon cancer is a highly relevant and important area of research.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1000
• Est. completion date: May 31, 2024
• Est. primary completion date: February 29, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 70 Years

Eligibility

Inclusion Criteria:

• over 18 years not pregnant not meeting any of the exclusion criteria Voluntary consent form signer * Indications for colonoscopy:

Colorectal cancer or adenomatous polyp in first-degree relatives Patients followed for more than 8 years with ulcerative colitis, Crohn's Disease, or individuals with a history of hereditary polyposis or non-polyposis syndrome. In these groups, the screening procedure should be started from the age of 40.

It is a population-based screening that begins at age 50 and ends at age 70 for all men and women (50 and 70 years will be included). However, especially in this group of patients;

Male patients presenting with iron deficiency anemia Female patients over 40 years of age presenting with iron deficiency anemia Patients with positive occult blood in stool in screening programs Patients presenting with rectal bleeding Patients with defecation irregularity, weight loss

Exclusion Criteria:

• under 18 years old

• Pregnant or planning to become

• Have another known diagnosis of gastrointestinal disease

• Abdominal surgery other than appendectomy or hysterectomy history

• Psychiatric comorbidity

• Chronic diseases that will affect the microbiome (cancer, diabetes, cardiovascular disease, liver diseases, neurological diseases, etc.)

• Use of drugs that may affect digestive function (including use in the last 4 weeks), probiotics, narcotic analgesics, lactulose (prebiotics) in the 4 weeks before the study

• Patients taking dietary supplements will not be included in the study.

Related Conditions & MeSH terms

• Colon Cancer
• Colonic Neoplasms
• Colonoscopy
• Microbiota

Intervention

Diagnostic Test:
• Artificial Intelligence-assisted Fecal Microbiome Testing
Next-generation sequencing of fecal samples and artificial intelligence analysis of test results

Procedure:
• Colonoscopy
Colonoscopy procedure

Locations

Country	Name	City	State
Turkey	Medipol University Esenler Hospital	Istanbul	Other (Non U.s.)

Sponsors (6)

Lead Sponsor	Collaborator
Istanbul Medipol University Hospital	Bozyaka Training and Research Hospital, Bursa City Hospital, Izmir Metropolitan Municipality Esrefpasa Hospital, SB Istanbul Education and Research Hospital, Tepecik Training and Research Hospital

Country where clinical trial is conducted

Turkey, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The diagnostic accuracy of the AI-assisted fecal microbiome testing in detecting colon cancer compared to colonoscopy	The diagnostic accuracy of the AI-assisted fecal microbiome testing in detecting colon cancer, as measured by sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC-ROC).	2 weeks