AI-Based Prediction of Stage and Survival in Non-Small Cell Lung Cancer: A Retrospective Study
NCT ID: NCT07068139
Last Updated: 2025-08-08
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ACTIVE_NOT_RECRUITING
150 participants
OBSERVATIONAL
2010-01-01
2025-09-01
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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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.
AI-Based Predictive Modeling
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.
Interventions
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AI-Based Predictive Modeling
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.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
* 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
Exclusion Criteria
* 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
18 Years
ALL
No
Sponsors
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Hilkat Fatih Elverdi
OTHER
Responsible Party
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Hilkat Fatih Elverdi
Thoracic Surgery Resident
Other Identifiers
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B.30.2.ODM.0.20.08/194-318
Identifier Type: -
Identifier Source: org_study_id
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