Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06270992 |
| Other study ID # |
123R030 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
November 15, 2023 |
| Est. completion date |
May 15, 2026 |
Study information
| Verified date |
February 2024 |
| Source |
TC Erciyes University |
| Contact |
Aycan Gundogdu, PhD |
| Phone |
+90 352 207 6666 |
| Email |
agundogdu[@]erciyes.edu.tr |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational [Patient Registry]
|
Clinical Trial Summary
The study aims to develop a deep learning-based diagnostic method for lung cancer using the
oral microbiome. This innovative approach involves establishing an observational cohort of
576 individuals, including lung cancer patients, non-cancerous benign lung disease patients,
and healthy controls, to collect tongue swab samples for 16S rRNA sequencing. Additionally,
an international cohort of approximately 1700 individuals will be formed using in silico
data. The project will utilize deep learning methods to analyze all data integratively and
develop an AI diagnostic algorithm capable of distinguishing lung cancer patients from
others. The diagnostic method's performance will be tested in a pilot clinical trial with 96
individuals using a PRoBE design. Led by experts in chest surgery, molecular microbiology,
and bioinformatics, the project spans over 30 months and aims to create a non-invasive,
easily accessible lung cancer screening method that could lead to significant diagnostic
advancements and potential spin-off companies in the field of liquid biopsy/molecular
diagnosis.
Description:
Cancer is a global health issue that is on an increasing trend in terms of incidence and
mortality rates, hindering the increase in life expectancy. According to the World Health
Organization, lung, colorectal, and liver cancers are among the most common causes of
cancer-related deaths. In Turkey, the incidence and mortality rates of lung cancer are higher
than the world average. are among the risk factors that may increase the risk of lung cancer.
In addition to risk factors like family history, smoking, different studies have shown that
dysbiotic oral microbiome may contribute to the risk of lung cancer.
The oral microbiome is the second most diverse microenvironment in our body and has been
associated with many diseases, including lung cancer. Studies to date on lung cancer-oral
microbiome have generally involved designs aimed at resolving cause-and-effect relationships
through statistical differences and/or mechanisms involving microbiome units.
However, there is no literature on any study aimed at developing a deep learning-based
diagnostic method that focuses on the oral microbiome.Therefore, the proposed study aims to
develop a microbiome based deep learning diagnostic method for lung cancer diagnosis. To this
end, an observation cohort will be established consisting of 192 lung cancer patients, 192
non-cancerous benign lung disease patients, and 192 healthy controls. Tongue swab samples
belonging to the cohort will be collected, and 16S rRNA sequencing will be performed. At the
same time, an international observation cohort of approximately 1700 individuals will be
created using in silico data. All data will be analyzed integratively, and an artificial
intelligence diagnostic algorithm that can differentiate lung cancer patients from other lung
diseases and healthy individuals will be developed using deep learning methods. In the final
stage, the performance of the diagnostic method developed for a pilot clinical trial cohort
of 96 individuals will be tested using a PRoBE (prospective specimen collection before
outcome ascertainment and retrospective blinded evaluation) design.
The original aspects of the project are the proposal of a novel design in the literature, the
creation of an experimental design/clinical trial and the presentation of a potential
solution proposal that may have a high impact on an important diagnostic problem.
If the project is successfully completed, an artificial intelligence-based method that can
potentially diagnose lung cancer through non-invasive oral microbiome samples will be
developed. In addition to its patentability, if the method is further developed (in the
medium to long term) into a product, it will enable lung cancer screening to be easily
performed even in primary healthcare institutions with a simple oral swab sample.
