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

NCT05907577 | Unknown

• 1 other identifier: 3023226
• Study Type: observational
• Target: 7,500
• Locations: 1 country
• Sites: 2

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.

Conditions

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

Outcome Measures

Primary Outcomes (1)

• Develop a model with high predictive performance for early detection of non-small cell lung cancer (NSCLC) in the eligible patient population.

  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.

  11 years

Secondary Outcomes (1)

• Demonstrate that the newly developed model achieves higher prediction accuracy than the well-validated model PLCOm2012.

  11 years

Other Outcomes (4)

• Develop a model with high predictive performance for early detection of small cell lung cancer (SCLC) in the eligible patient population.

  11 years

• Develop a model for prediction of lung cancer in a time period when the disease is still highly unlikely to be clinically detectable, in a subset of patients who meet the extended eligibility criteria.

  11 years

• Identify features with the highest discriminatory power of lung cancer prediction and early detection.

  11 years

Study Arms (2)

Disease cohort

Observational, no interventions

Other: Observational

Control cohort

Observational, no interventions

Other: Observational

Interventions

Observational OTHER

No interventions.

Control cohort; Disease cohort

Eligibility Criteria

• Age: 50 Years - 79 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study will include adult active or former smokers who are at high-risk of developing lung cancer, and would be considered suitable candidates for lung cancer screening. The study will focus on patients with confirmed bronchogenic lung cancer.

Sponsors & Collaborators

• The University Clinic of Pulmonary and Allergic Diseases Golnik: lead
• Jozef Stefan Institute: collaborator

Study Sites (2)

University Clinic of Respiratory and Allergic Diseases Golnik

Golnik, 4204, Slovenia

Location

Jozef Stefan Institute

Ljubljana, 1000, Slovenia

Location

MeSH Terms

Conditions

Adenocarcinoma of Lung; Bronchial Neoplasms

Interventions

Watchful Waiting

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Lung Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Bronchial Diseases; Respiratory Tract Diseases

Intervention Hierarchy (Ancestors)

Outcome Assessment, Health Care; Outcome and Process Assessment, Health Care; Quality of Health Care; Health Services Administration

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assist Prof Aleš Rozman, MD, PhD

Study Record Dates

• First Submitted: June 8, 2023
• First Posted: June 18, 2023
• Study Start: September 1, 2023
• Primary Completion: September 1, 2024
• Study Completion: September 1, 2024
• Last Updated: June 18, 2023

Record last verified: 2023-06

Data Sharing

• IPD Sharing: Will not share

Locations

SL (2)