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Early Detection of Lung Cancer With Machine Learning Based on Routine Clinical Investigations

NCT05907577 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 7500

Last updated 2023-06-18

No results posted yet for this study

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.

Conditions

  • Adenocarcinoma of Lung; Bronchial Neoplasms; Early Detection of Cancer; Machine Learning

Interventions

OTHER

Observational

No interventions.

Sponsors & Collaborators

  • Jozef Stefan Institute

    collaborator OTHER

  • The University Clinic of Pulmonary and Allergic Diseases Golnik

    lead OTHER

Eligibility

Min Age
50 Years
Max Age
79 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-09-01
Primary Completion
2024-09-01
Completion
2024-09-01

Countries

  • Slovenia

Study Locations

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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 NCT05907577 on ClinicalTrials.gov