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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Adenocarcinoma of Lung; Bronchial Neoplasms; Early Detection of Cancer; Machine Learning
• Intervention / Treatment: Other: Observational

• Study Type: Observational
• Enrollment (Estimated): 7500

Contacts and Locations

Study Locations

• Slovenia
  • Golnik, Slovenia, 4204
    • University Clinic of Respiratory and Allergic Diseases Golnik
    • Contact: Ales Rozman, MD, PhD; Email: ales.rozman@klinika-golnik.si
    • Principal Investigator: Mitja Prah, MD
  • Ljubljana, Slovenia, 1000
    • Jozef Stefan Institute
    • Contact: Mitja Luštrek, PhD; Email: mitja.lustrek@ijs.si
    • Principal Investigator: Mitja Luštrek, PhD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• 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.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Disease cohort; Observational, no interventions	Other: Observational; No interventions.
Control cohort; Observational, no interventions	Other: Observational; No interventions.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Time Frame
Demonstrate that the newly developed model achieves higher prediction accuracy than the well-validated model PLCOm2012.	11 years

Other Outcome Measures

Outcome Measure	Time Frame
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
Identify features with the highest discriminatory power to distinguish between lung cancer patients in stage I-II and stage III-IV.	11 years

Collaborators and Investigators

• Sponsor: The University Clinic of Pulmonary and Allergic Diseases Golnik
• Collaborators: Jozef Stefan Institute

Study record dates

Study Major Dates

• Study Start (Estimated): September 1, 2023
• Primary Completion (Estimated): September 1, 2024
• Study Completion (Estimated): September 1, 2024

Study Registration Dates

• First Submitted: June 8, 2023
• First Submitted That Met QC Criteria: June 8, 2023
• First Posted (Estimated): June 16, 2023

Study Record Updates

• Last Update Posted (Estimated): June 16, 2023
• Last Update Submitted That Met QC Criteria: June 8, 2023
• Last Verified: June 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms by Histologic Type; Neoplasms; Neoplasms by Site; Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Bronchial Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms; Adenocarcinoma of Lung; Bronchial Neoplasms
• Other Study ID Numbers: 3023226

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No