Neoplasms

Urinary Tract, Sexual Organs, and Pregnancy Conditions

All Conditions

Rare Diseases

Uterine Cervical Neoplasms

Uterine Cervical Dysplasia

Uterine Neoplasms

Genital Neoplasms, Female

Urogenital Neoplasms

Uterine Cervical Diseases

Uterine Diseases

Genital Diseases, Female

Genital Diseases

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Precancerous Conditions

Cervical Intraepithelial Neoplasia

Two Vaginal self sampling devices and HPV test

Screening test

Ken HAGUENOER

Scientific Context

High-risk types of human papillomavirus (HPV) are the causative agents for cervical cancer. Cervical cancer screening strategies rely on periodic Papanicolaou (Pap) testing. It's well-known that this test has significantly contributed to the reduction of mortality and morbidity due to cervical cancer. In France, it now seems that the screening strategy could be optimized. The two main ways are to reach the 7 million underscreened women (mass screening, self-sampling for HPV DNA testing) and to improve the screening test (HPV DNA testing, computer-assisted cytology). Self-collected vaginal samples (SCVS) for HPV DNA testing could be a relevant screening option: this technique appears reliable and it could allow to reach women who are never or seldom screened. Currently, there is no French data on the SCVS for HPV DNA testing.

The goal of this study is to determine the performance and acceptability of a population-based strategy using self-collected vaginal samples for HPV DNA testing to reach women who are not participating in cervical cancer screening.

Description of the project

This project (APACHE-1) is a part of a project called APACHE. APACHE-1 will be dedicated to the SCVS technical validation and to the comparison between different transports medium. Nine hundred women will be recruited from the 20 to 65 years consultants in the Tours University Hospital (CHRU of Tours) and in the IRSA (health centre for routine medical checkup).

Each woman will collect 2 SCVS with a nylon flocked swab (Copan® microRheologics™). The first SCVS will be put in a vial which contains a liquid medium. The second SCVS will be put in a dry vial. Then during the speculum examination the physician will collect a cervical sample with a Rovers® Cervex-Brush™ and then rinse it into a vial containing PréservCyt ™. A blind HPV DNA testing and genotyping (Inno-LIPA HPV Genotyping ™ Extra) will be performed in the virology laboratory of the CHRU of Tours on all samples (SCVSs and cervical samples performed by a physician). The performance of the SCVS to detect cervical HPV infection will be assessed (gold standard test = HPV DNA testing on cervical samples collected by the physician).

In a second time (APACHE-2), the researchers will investigate to what extent offering home obtained SCVS leads to participation of unscreened women. APACHE-2 will be registered on Clinicaltrials as another project.

Self-sampling and Human Papillomavirus (HPV)-Testing for Unscreened Women in Cervical Cancer Prevention

Human Papillomavirus DNA Testing Using Self-collected Samples for Cervical Cancer Screening: an Alternative Strategy for Unscreened Women?

University Hospital, Tours

Digestive System Diseases

Colonic Neoplasms

Colorectal Neoplasms

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Colonic Diseases

Intestinal Diseases

Herbal and Botanical

All Drugs and Chemicals

Cola

Fecal samples will be obtained from patients who are enrolled for colonoscopy procedures for the suspicion of colon cancer.

Next-generation sequencing of fecal samples and artificial intelligence analysis of test results

Artificial Intelligence-assisted Fecal Microbiome Testing

Colonoscopy

Varol TUNALI, Dr.

Naciye Cigdem Arslan, MD

Naciye Cigdem Arslan

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.

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.

Fecal samples will be obtained from patients who are enrolled for colonoscopy for the clinical suspicion of colon cancer

The patients will be blinded to the microbiome results for the study period. The gastroenterologists will be blinded to microbiome results. The microbiome researchers will be blinded to colonoscopy results The statisticians will be blinded to both intervention results until the end of patient enrollment

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

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

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).

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Self-sampling and Human Papillomavirus (HPV)-Testing for Unscreened Women in Cervical Cancer Prevention	NCT01014026	Uterine Cervica... Uterine Cervica...	Screening test	20 Years - 65 Years	University Hospital, Tours
Evaluation of the Diagnostic Potential of Artificial Intelligence-assisted Fecal Microbiome Testing for Colon Cancer	NCT05795725	Colon Cancer Microbiota Colonoscopy	Artificial Inte... Colonoscopy	18 Years - 70 Years	Istanbul Medipol University Hospital
Evaluation of the Diagnostic Potential of Artificial Intelligence-assisted Fecal Microbiome Testing for Colon Cancer	NCT05795725	Colon Cancer Microbiota Colonoscopy	Artificial Inte... Colonoscopy	18 Years - 70 Years	Istanbul Medipol University Hospital

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