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

NCT ID: NCT05907577

Last Updated: 2023-06-18

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 7500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-09-01
• Study Completion Date: 2024-09-01

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

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: RETROSPECTIVE

Study Groups

Disease cohort

Observational, no interventions

Observational

Intervention Type: OTHER

No interventions.

Control cohort

Observational, no interventions

Observational

Intervention Type: OTHER

No interventions.

Interventions

Observational

No interventions.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

All patients:

• Age ≥ 50 years and < 80 years at the index date of diagnosis (for Cases) or pseudodiagnosis (for Controls).
• Presence of at least one extended blood analysis, spirometry and DLCO report within the 6 months before the index date.
• Chest CT scan performed in a non-urgent setting (electively) within the 6 months before the index date (= index CT).
• Active smokers at the index date or former smokers that ceased smoking within 15 years before the index date.
• Smoking history ≥ 20 pack-years.

Additional for Cases only: Confirmed histological diagnosis of bronchogenic lung cancer in the time period ≥ 2010 and ≤ 2020.

Additional for Controls only:

• Absence of lung cancer at all times ≤ 2020, confirmed by chest CT scan at the index date.
• Documented to live without diagnosis of lung cancer for at least 3 years after the index date.

Extended criteria for the lung cancer prediction subgroup:

In addition to the above stated inclusion criteria, patients in this subgroup have at least one extended blood analysis, spirometry and DLCO report available in the time interval between 3-5 years before the index date.

• Minimum Eligible Age: 50 Years
• Maximum Eligible Age: 79 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Jozef Stefan Institute

OTHER

Sponsor Role: collaborator

The University Clinic of Pulmonary and Allergic Diseases Golnik

OTHER

Sponsor Role: lead

Responsible Party

Aleš Rozman

Assist Prof Aleš Rozman, MD, PhD

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

University Clinic of Respiratory and Allergic Diseases Golnik

Golnik, , Slovenia

Jozef Stefan Institute

Ljubljana, , Slovenia

Countries

Slovenia

Facility Contacts

Ales Rozman, MD, PhD

Role: primary

ales.rozman@klinika-golnik.si

Mitja Luštrek, PhD

Role: primary

mitja.lustrek@ijs.si

Other Identifiers

3023226

Identifier Type: -

Identifier Source: org_study_id