Lung CancerNCT06270992
Oral Microbiome Diagnostics of Lung Cancer
An observational study of Lung Cancer, run by TC Erciyes University at 1 site in Turkey (Türkiye).
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… Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Lung Cancer
- What is being tested?
- NCCN (National Comprehensive Cancer Network) diagnosis
- Type of treatment
- Pill or oral medicine
- How many people
- 676 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 15 Nov 2023to 15 May 2026
- How long will I take part?
- 12 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The population consists of a lung cancer group of 192 patients, a non-lung cancer benign lung disease group of 192 patients, and 292 healthy controls. Each subgroups are gender and age matched.
Who may be able to join
- To be between the ages of 18 and 65,
- Not to have a diagnosed lung disease or suspicion thereof,
- Not to have complaints related to the lungs and/or respiratory tract,
- Not to have alcohol or severe substance dependency,
- Not having a hospitalization history in the last year,
- Not having used antibiotics in the last six months,
Show all 10Show fewer
- Not having used products manufactured to support the oral microbiome, such as probiotics (lozenges, sublingual drops) for at least the last six months,
- Not being pregnant or breastfeeding,
- Not having undergone dental procedures such as root canal treatment, implants, prostheses, tooth extraction, fillings in the last 6 months
- Not having dominant immune-origin lesions (such as aphthous ulcers, erythema multiforme, pemphigus), viral-origin lesions (such as herpes, Koplik spots, herpangina), dominant bacterial infections like tonsillitis, and/or thermal or chemical mucosal traumas in the mouth.
Who cannot join
- Not to satisfy inclusion or declining to participate even though all the inclusion criteria are satisfied.
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site, 1 recruiting
- Recruiting
Erciyes University Hospital
Kayseri, 38039
Aycan Gundogdu, PhD, +90 352 207 6666, agundogdu@erciyes.edu.tr
About this study
Official title: Developing an Artificial Intelligence-based Diagnostic Method Based on Oral Microbiome for Non-invasive Diagnosis of Lung Cancer
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.
Read the 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 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.
- Conditions
- Lung Cancer
- Being tested
- NCCN (National Comprehensive Cancer Network) diagnosis
- Keywords
- oral microbiome, non-invasive diagnosis, artificial intelligence
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
NCCN (National Comprehensive Cancer Network) diagnosisDiagnostic test
For diagnostic evaluation, the necessary procedures from the standard protocols consisting of anamnesis, physical examination, laboratory tests, radiological imaging methods, and tissue biopsy will be followed. Computerized Tomography (CT) and Positron Emission Tomography-Computed Tomography (PET-CT) will be used as imaging methods, while fiberoptic bronchoscopy and video-assisted mediastinoscopy will be applied for tissue diagnosis and staging.
| Study group | Role | What they receive |
|---|---|---|
| Lung cancer group | 192 patients diagnosed with lung cancer with the following inclusion criteria:
* To be between the ages of 18 and 65,
* Not having been previously diagnosed with and treated for lung cancer,
* Not having received any cancer treatment in the last 2 years,
* Not having been operated on within the last two years,
* Not having a hospitalization history in the last year,
* Not having used antibiotics in the last six months,
* Not having used products manufactured to support the oral microbiome, such as probiotic (lozenges, sublingual drops) for at least the last six months,
* Not being pregnant, not breastfeeding,
* Not having undergone dental procedures such as root canal treatment, implants, prostheses, tooth extraction, fillings in the last 6 months, Not having dominant immune-origin lesions , viral-origin lesions, dominant bacterial infections like tonsillitis, and/or thermal or chemical mucosal traumas in the mouth. Receives: Diagnostic Test: NCCN (National Comprehensive Cancer Network) diagnosis | |
| Benign lung disease group | 192 patients diagnosed with non-cancer diseases with the following inclusion criteria:
* To be between the ages of 18 and 65,
* To have visited our clinic with complaints related to the lungs and/or respiratory tract,
* Not having been diagnosed with lung cancer after clinical evaluation,
* Not having received any cancer treatment in the last 2 years,
* Not having been operated on within the last two years,
* Not having a hospitalization history in the last year,
* Not having used antibiotics in the last six months,
* Not having used products manufactured to support the oral microbiome, such as probiotics for at least the last six months,
* Not being pregnant or breastfeeding,
* Not having undergone dental procedures such as root canal treatment, implants, prostheses, tooth extraction, fillings in the last 6 months,
* Not having dominant immune-origin lesions, viral-origin lesions, dominant bacterial infections like tonsillitis, and/or thermal or chemical mucosal traumas in the mouth. Receives: Diagnostic Test: NCCN (National Comprehensive Cancer Network) diagnosis | |
| Healthy control group | 292 individuals without lung cancer or another lung disease diagnosis with the following inclusion criteria: * To be between the ages of 18 and 65, * Not to have a diagnosed lung disease or suspicion thereof, * Not to have complaints related to the lungs and/or respiratory tract, * Not to have alcohol or severe substance dependency, * Not having a hospitalization history in the last year, * Not having used antibiotics in the last six months, * Not having used products manufactured to support the oral microbiome, such as probiotics (lozenges, sublingual drops) for at least the last six months, * Not being pregnant or breastfeeding, * Not having undergone dental procedures such as root canal treatment, implants, prostheses, tooth extraction, fillings in the last 6 months, * Not having dominant immune-origin lesions, viral-origin lesions, dominant bacterial infections like tonsillitis, and/or thermal or chemical mucosal traumas in the mouth. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostics accuracy assessment
Diagnostic technology under investigation will be evaluated using sensitivity, specificity, area under receiver operating curve. Cross validation will be used for testing and NCCN diagnosis and patient follow-ups will be considered as the ground truth.
Measured: 30 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Feb 2024
- Study start15 Nov 2023
- Primary completion15 Feb 2025 (estimated)
- Study completion15 May 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by TC Erciyes UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 676 participants (estimated)
- Target duration
- 12 Months
Sponsor and investigators
- Lead sponsor
- TC Erciyes University
- Sponsor type
- University, hospital or other
- Collaborators
- The Scientific and Technological Research Council of Turkey
- Responsible party
- Principal investigator, Aycan Gundogdu, TC Erciyes University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06270992
- Sponsor's protocol ID
- 123R030
- Status last verified
- Feb 2024
- First submitted
- 14 Feb 2024
- Last updated
- 21 Feb 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes
Study at a glance
From the registry record, last updated by the sponsor on 21 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Developing an Artificial Intelligence-based Diagnostic Method Based on Oral Microbiome for Non-invasive Diagnosis of Lung Cancer
- ClinicalTrials.gov ID
- NCT06270992
- Acronym / sponsor ID
- 123R030
- Conditions
- Lung Cancer
- Keywords
- oral microbiome, non-invasive diagnosis, artificial intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 676 participants (estimated)
- Lead sponsor
- TC Erciyes UniversityUniversity, hospital or other
- Collaborators
- The Scientific and Technological Research Council of Turkey
- Study start
- 15 Nov 2023 (actual)
- Primary completion
- 15 Feb 2025 (estimated)
- Study completion
- 15 May 2026 (estimated)
- First posted
- 21 Feb 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 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.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Target follow-up
- 12 Months
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Lung cancer group | – | Diagnostic Test: NCCN (National Comprehensive Cancer Network) diagnosis192 patients diagnosed with lung cancer with the following inclusion criteria: * To be between the ages of 18 and 65, * Not having been previously diagnosed with and treated for lung cancer, * Not having received any cancer treatment in the last 2 years, * Not having been operated on within the last two years, * Not having a hospitalization history in the last year, * Not having used antibiotics in the last six months, * Not having used products manufactured to support the oral microbiome, such as probiotic (lozenges, sublingual drops) for at least the last six months, * Not being pregnant, not breastfeeding, * Not having undergone dental procedures such as root canal treatment, implants, prostheses, tooth extraction, fillings in the last 6 months, Not having dominant immune-origin lesions , viral-origin lesions, dominant bacterial infections like tonsillitis, and/or thermal or chemical mucosal traumas in the mouth. |
| Benign lung disease group | – | Diagnostic Test: NCCN (National Comprehensive Cancer Network) diagnosis192 patients diagnosed with non-cancer diseases with the following inclusion criteria: * To be between the ages of 18 and 65, * To have visited our clinic with complaints related to the lungs and/or respiratory tract, * Not having been diagnosed with lung cancer after clinical evaluation, * Not having received any cancer treatment in the last 2 years, * Not having been operated on within the last two years, * Not having a hospitalization history in the last year, * Not having used antibiotics in the last six months, * Not having used products manufactured to support the oral microbiome, such as probiotics for at least the last six months, * Not being pregnant or breastfeeding, * Not having undergone dental procedures such as root canal treatment, implants, prostheses, tooth extraction, fillings in the last 6 months, * Not having dominant immune-origin lesions, viral-origin lesions, dominant bacterial infections like tonsillitis, and/or thermal or chemical mucosal traumas in the mouth. |
| Healthy control group | – | –292 individuals without lung cancer or another lung disease diagnosis with the following inclusion criteria: * To be between the ages of 18 and 65, * Not to have a diagnosed lung disease or suspicion thereof, * Not to have complaints related to the lungs and/or respiratory tract, * Not to have alcohol or severe substance dependency, * Not having a hospitalization history in the last year, * Not having used antibiotics in the last six months, * Not having used products manufactured to support the oral microbiome, such as probiotics (lozenges, sublingual drops) for at least the last six months, * Not being pregnant or breastfeeding, * Not having undergone dental procedures such as root canal treatment, implants, prostheses, tooth extraction, fillings in the last 6 months, * Not having dominant immune-origin lesions, viral-origin lesions, dominant bacterial infections like tonsillitis, and/or thermal or chemical mucosal traumas in the mouth. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The population consists of a lung cancer group of 192 patients, a non-lung cancer benign lung disease group of 192 patients, and 292 healthy controls. Each subgroups are gender and age matched.
Key inclusion criteria (10 listed)
- To be between the ages of 18 and 65,
- Not to have a diagnosed lung disease or suspicion thereof,
- Not to have complaints related to the lungs and/or respiratory tract,
- Not to have alcohol or severe substance dependency,
- Not having a hospitalization history in the last year,
Key exclusion criteria (1 listed)
- Not to satisfy inclusion or declining to participate even though all the inclusion criteria are satisfied.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| NCCN (National Comprehensive Cancer Network) diagnosisFor diagnostic evaluation, the necessary procedures from the standard protocols consisting of anamnesis, physical examination, laboratory tests, radiological imaging methods, and tissue biopsy will be followed. Computerized Tomography (CT) and Positron Emission Tomography-Computed Tomography (PET-CT) will be used as imaging methods, while fiberoptic bronchoscopy and video-assisted mediastinoscopy will be applied for tissue diagnosis and staging. | Diagnostic test | Benign lung disease group, Lung cancer group |
Primary outcome
Diagnostics accuracy assessment
Time frame: 30 months
Diagnostic technology under investigation will be evaluated using sensitivity, specificity, area under receiver operating curve. Cross validation will be used for testing and NCCN diagnosis and patient follow-ups will be considered as the ground truth.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Feb 2024.
- Individual participant dataSharing planned: Yes
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer, run by TC Erciyes University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 10 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 21 Feb 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Aycan Gundogdu, PhD (+90 352 207 6666, agundogdu@erciyes.edu.tr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostics accuracy assessment (time frame: 30 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Feb 2024.