Ignite Creation Date:
2024-05-06 @ 8:09 PM
Last Modification Date:
2024-10-26 @ 3:21 PM
Study NCT ID:
NCT06270992
Status:
RECRUITING
Last Update Posted:
2024-02-21
First Post:
2024-02-14
Brief Title:
Oral Microbiome Diagnostics of Lung Cancer
Sponsor:
TC Erciyes University
Organization:
TC Erciyes University
Study Overview
Official Title:
Developing an Artificial Intelligence-based Diagnostic Method Based on Oral Microbiome for Non-invasive Diagnosis of Lung Cancer
Status:
RECRUITING
Status Verified Date:
2024-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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 methods 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 biopsymolecular diagnosis
Detailed 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 andor 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 microbiomeTherefore 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 designclinical 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
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 andor 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 microbiomeTherefore 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 designclinical 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None