Clinical trial · Observational
AI-Based Prediction of Stage and Survival in Non-Small Cell Lung Cancer: A Retrospective Study
The Role of Artificial Intelligence in Predicting Stage and Survival in Non-Small Cell Lung Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence (AI) in Diagnosis | — | UNRESOLVED | — |
| Non-Small Cell Lung Cancer | Lung Non-Small Cell Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-Based Predictive Modeling | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- NSCLC Surgery Cohort
- description
- 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.
- interventionNames
- Other: AI-Based Predictive Modeling
Primary outcomes (1)
- measure
- Development of AI Model for Predicting Tumor Stage and Survival
- timeFrame
- From data extraction to completion of model training and validation (estimated by September 2025)
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * 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 Exclusion Criteria: * 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
References
Publications (0)
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