Early Detection of Lung Cancer With Machine Learning Based on Routine Clinical Investigations
- Conditions
- Adenocarcinoma of Lung; Bronchial Neoplasms; Early Detection of Cancer; Machine Learning
- Interventions
- Other: Observational
- Registration Number
- NCT05907577
- Lead Sponsor
- The University Clinic of Pulmonary and Allergic Diseases Golnik
- Brief Summary
This observational, cross-sectional study in lung cancer patients and lung cancer-free controls aims to develop a machine learning model for early detection of LC based on routine, widely accessible and minimally invasive clinical investigations. The model with adequate predictive performance could later be used in clinical practice as an aid in defining the optimal population and timing for lung cancer screening program.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 7500
Not provided
Not provided
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Disease cohort Observational Observational, no interventions Control cohort Observational Observational, no interventions
- Primary Outcome Measures
Name Time Method Develop a model with high predictive performance for early detection of non-small cell lung cancer (NSCLC) in the eligible patient population. 11 years The primary outcome is tested by calculating a joint rectangular 95% confidence region for {sensitivity, specificity} and compared with the reported accuracy of NLST study screening criteria.
- Secondary Outcome Measures
Name Time Method Demonstrate that the newly developed model achieves higher prediction accuracy than the well-validated model PLCOm2012. 11 years
Trial Locations
- Locations (2)
University Clinic of Respiratory and Allergic Diseases Golnik
🇸🇮Golnik, Slovenia
Jozef Stefan Institute
🇸🇮Ljubljana, Slovenia