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AI-Based Prediction of Stage and Survival in Non-Small Cell Lung Cancer: A Retrospective Study

NCT07068139 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 150

Last updated 2025-08-08

No results posted yet for this study

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 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.

Conditions

Interventions

OTHER

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.

Sponsors & Collaborators

  • Hilkat Fatih Elverdi

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2010-01-01
Primary Completion
2025-09-01
Completion
2025-09-01

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Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07068139 on ClinicalTrials.gov