Non-Small Cell Lung CancerNCT07068139
AI-Based Prediction of Stage and Survival in Non-Small Cell Lung Cancer: A Retrospective Study
An observational study of Non-Small Cell Lung Cancer and Artificial Intelligence (AI) in Diagnosis, run by Hilkat Fatih Elverdi.
Trial summary
This study aims to evaluate the role of artificial intelligence (AI) in predicting disease stage and survival in patients diagnosed with non-small cell lung cancer (NSCLC). Using a retrospective design, the research will analyze radiologic imaging data (PET-CT and chest CT) and corresponding histopathological results… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Non-Small Cell Lung Cancer, Artificial Intelligence (AI) in Diagnosis
- What is being tested?
- AI-Based Predictive Modeling
- How many people
- 156 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2010to 1 Jun 2026
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study includes patients who underwent surgical treatment for non-small cell lung cancer (NSCLC) at Ondokuz Mayis University Hospital in Samsun, Türkiye. Eligible patients are selected from the hospital's electronic medical records and radiologic imaging archive between January 2010 and March 2025. The population reflects a clinical sample from a tertiary referral center serving a diverse adult patient population in the Black Sea region of Türkiye.
Who may be able to join
- Age ≥ 18 years
- Diagnosed with non-small cell lung cancer (NSCLC)
- Underwent surgical treatment for NSCLC at Ondokuz Mayis University Hospital
- Available preoperative PET-CT and chest CT imaging
- Available postoperative histopathological diagnosis and staging
- Signed informed consent form for data use in research
Who cannot join
- Age < 18 years
- No available PET-CT or chest CT imaging in hospital records
- No available histopathological diagnosis in hospital records
- Diagnosed with a type of lung cancer other than NSCLC
- Patients who did not undergo surgery
- Patients who did not provide informed consent for retrospective data use
About this study
Official title: The Role of Artificial Intelligence in Predicting Stage and Survival in Non-Small Cell Lung Cancer
This study aims to evaluate the role of artificial intelligence (AI) in predicting disease stage and survival in patients diagnosed with non-small cell lung cancer (NSCLC). Using a retrospective design, the research will analyze radiologic imaging data (PET-CT and chest CT) and corresponding histopathological results of patients who underwent lung cancer surgery at Ondokuz Mayis University Hospital.
The goal is to develop and validate a deep learning-based AI model that can automatically assess preoperative radiologic features and estimate postoperative tumor stage and survival outcomes. By integrating radiologic data with confirmed pathological diagnoses, the AI system is expected to provide clinical decision support that can improve diagnostic speed, reduce human error, and help clinicians predict prognosis more accurately.
This study does not involve any experimental treatment or prospective follow-up of patients. All data will be collected from existing medical records. The findings may contribute to the digital transformation of healthcare and promote the use of AI tools in thoracic oncology.
- Being tested
- AI-Based Predictive Modeling
- Keywords
- Non-Small Cell Lung Cancer, Artificial Intelligence, Deep Learning, Radiology, Thoracic Surgery
- Medical subject headings
- Carcinoma, Non-Small-Cell Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Predictive ModelingOther
Also called Deep Learning Algorithm, Retrospective Imaging Analysis
This is not a therapeutic or diagnostic intervention. The study uses a retrospective dataset of radiologic and pathological records to train and validate a deep learning model designed to predict tumor stage and survival in patients with non-small cell lung cancer (NSCLC). No experimental procedure is applied to participants.
| Study group | Role | What they receive |
|---|---|---|
| NSCLC Surgery Cohort | This cohort includes patients who were diagnosed with non-small cell lung cancer (NSCLC) and underwent surgical treatment at Ondokuz Mayis University Hospital. Preoperative PET-CT and chest CT images and corresponding postoperative histopathological data were retrospectively collected and analyzed to develop an artificial intelligence model for predicting tumor stage and survival. Receives: Other: AI-Based Predictive Modeling |
What is being measured
The primary outcome is the main question the study is designed to answer.
Development of AI Model for Predicting Tumor Stage and Survival
The primary outcome of this study is to develop and validate a deep learning-based artificial intelligence model that can predict postoperative tumor stage and survival in patients with non-small cell lung cancer using preoperative PET-CT and chest CT imaging data. The primary outcome will be considered achieved when at least 80% of the planned patient dataset (150 patients) has been successfully included and used for model development.
Measured: From data extraction to completion of model training and validation (estimated by September 2025)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jul 2025
- Study start1 Jan 2010
- Primary completion1 May 2026
- Study completion1 Jun 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Non-Small Cell Lung CancerStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 156 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hilkat Fatih Elverdi
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Hilkat Fatih Elverdi, Ondokuz Mayıs University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07068139
- Sponsor's protocol ID
- B.30.2.ODM.0.20.08/194-318
- Status last verified
- Jul 2025
- First submitted
- 7 Jul 2025
- Last updated
- 15 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: De-identified individual participant data (e.g., imaging features, demographic variables, survival outcomes) may be shared for academic and research purposes upon reasonable request and following institutional data-sharing agreements. No personal identifiers will be included.
Study at a glance
From the registry record, last updated by the sponsor on 15 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Role of Artificial Intelligence in Predicting Stage and Survival in Non-Small Cell Lung Cancer
- ClinicalTrials.gov ID
- NCT07068139
- Acronym / sponsor ID
- B.30.2.ODM.0.20.08/194-318
- Keywords
- Non-Small Cell Lung Cancer, Artificial Intelligence, Deep Learning, Radiology, Thoracic Surgery
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 156 participants (actual)
- Lead sponsor
- Hilkat Fatih ElverdiUniversity, hospital or other
- Study start
- 1 Jan 2010 (actual)
- Primary completion
- 1 May 2026 (actual)
- Study completion
- 1 Jun 2026 (actual)
- First posted
- 16 Jul 2025 (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.
This study aims to evaluate the role of artificial intelligence (AI) in predicting disease stage and survival in patients diagnosed with non-small cell lung cancer (NSCLC). Using a retrospective design, the research will analyze radiologic imaging data (PET-CT and chest CT) and corresponding histopathological results of patients who underwent lung cancer surgery at Ondokuz Mayis University Hospital.
The goal is to develop and validate a deep learning-based AI model that can automatically assess preoperative radiologic features and estimate postoperative tumor stage and survival outcomes. By integrating radiologic data with confirmed pathological diagnoses, the AI system is expected to provide clinical decision support that can improve diagnostic speed, reduce human error, and help clinicians predict prognosis more accurately.
This study does not involve any experimental treatment or prospective follow-up of patients. All data will be collected from existing medical records. The findings may contribute to the digital transformation of healthcare and promote the use of AI tools in thoracic oncology.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| NSCLC Surgery Cohort | – | Other: AI-Based Predictive ModelingThis cohort includes patients who were diagnosed with non-small cell lung cancer (NSCLC) and underwent surgical treatment at Ondokuz Mayis University Hospital. Preoperative PET-CT and chest CT images and corresponding postoperative histopathological data were retrospectively collected and analyzed to develop an artificial intelligence model for predicting tumor stage and survival. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study includes patients who underwent surgical treatment for non-small cell lung cancer (NSCLC) at Ondokuz Mayis University Hospital in Samsun, Türkiye. Eligible patients are selected from the hospital's electronic medical records and radiologic imaging archive between January 2010 and March 2025. The population reflects a clinical sample from a tertiary referral center serving a diverse adult patient population in the Black Sea region of Türkiye.
Key inclusion criteria (6 listed)
- Age ≥ 18 years
- Diagnosed with non-small cell lung cancer (NSCLC)
- Underwent surgical treatment for NSCLC at Ondokuz Mayis University Hospital
- Available preoperative PET-CT and chest CT imaging
- Available postoperative histopathological diagnosis and staging
Key exclusion criteria (6 listed)
- Age < 18 years
- No available PET-CT or chest CT imaging in hospital records
- No available histopathological diagnosis in hospital records
- Diagnosed with a type of lung cancer other than NSCLC
- Patients who did not undergo surgery
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 |
|---|---|---|
| AI-Based Predictive ModelingAlso: Deep Learning Algorithm, Retrospective Imaging AnalysisThis is not a therapeutic or diagnostic intervention. The study uses a retrospective dataset of radiologic and pathological records to train and validate a deep learning model designed to predict tumor stage and survival in patients with non-small cell lung cancer (NSCLC). No experimental procedure is applied to participants. | Other | NSCLC Surgery Cohort |
Primary outcome
Development of AI Model for Predicting Tumor Stage and Survival
Time frame: From data extraction to completion of model training and validation (estimated by September 2025)
The primary outcome of this study is to develop and validate a deep learning-based artificial intelligence model that can predict postoperative tumor stage and survival in patients with non-small cell lung cancer using preoperative PET-CT and chest CT imaging data. The primary outcome will be considered achieved when at least 80% of the planned patient dataset (150 patients) has been successfully included and used for model development.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jul 2026.
- Individual participant dataSharing planned: Yes. De-identified individual participant data (e.g., imaging features, demographic variables, survival outcomes) may be shared for academic and research purposes upon reasonable request and following institutional data-sharing agreements. No personal identifiers will be included.
Related trials for Non-Small Cell Lung Cancer
RecruitingPhase 1/2
Study of INBRX-106 and INBRX-106 in Combination With Pembrolizumab (Keytruda®) in Subjects With Locally Advanced or Metastatic Solid Tumors (Hexavalent OX40 Agonist)
Solid Tumor, Non-Small Cell Lung Cancer
RecruitingObservational study
A Prospective ,Multicenter ,Non-interventional ,Real-world Study Observing Changes in Molecular Markers Osimertinib Targeted Therapy in EGFRm NSCLC
Non Small Cell Lung Cancer
RecruitingPhase 3
A Study Evaluating Sotorasib Platinum Doublet Combination Versus Pembrolizumab Platinum Doublet Combination as a Front-Line Therapy in Participants With Stage IV or Advanced Stage IIIB/C Nonsquamous Non-Small Cell Lung Cancers (CodeBreaK 202)
Non-Small Cell Lung Cancer (NSCLC)
Active, not recruitingPhase 2
Datopotamab Deruxtecan (Dato-DXd, DS-1062a) in Advanced and/or Unresectable Non-Small Cell Lung Cancer
Metastatic Lung Cancer, Advanced Non Small Cell Lung Cancer
Not yet recruitingPhase 1/2
STAR0602 With Nivolumab in Non Small Cell Lung Cancer and in Metastatic Castrate-Resistant Prostate Cancer, and STAR0602 Alone in Thymic Carcinoma
NSCLC, Non-Small Cell Lung Cancer
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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Non-Small Cell Lung Cancer and Artificial Intelligence (AI) in Diagnosis, run by Hilkat Fatih Elverdi.
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 6 inclusion and 6 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: finished, as of the sponsor's last update on 15 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development of AI Model for Predicting Tumor Stage and Survival (time frame: From data extraction to completion of model training and validation (estimated by September 2025)). 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 15 Jul 2026.