Clinical trial NCT05907577: Early Detection of Lung Cancer With Machine Learning Based on Routine Clinical Investigations
Official title: Early Detection of Lung Cancer With Machine Learning Based on Routine Clinical Investigations
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Adenocarcinoma of Lung; Bronchial Neoplasms; Early Detection of Cancer; Machine Learning
Lead sponsor: The University Clinic of Pulmonary and Allergic Diseases Golnik
Sponsor class: OTHER
Collaborators: name: Jozef Stefan Institute
First posted: 2023-06-18
Enrollment: 7500
Sex: ALL
Minimum age (years): 50
Maximum age (years): 79
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
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.

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.


Eligibility 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.

Primary outcomes:
measure: Develop a model with high predictive performance for early detection of non-small cell lung cancer (NSCLC) in the eligible patient population.
timeFrame: 11 years
Secondary outcomes:
measure: Demonstrate that the newly developed model achieves higher prediction accuracy than the well-validated model PLCOm2012.
timeFrame: 11 years
Other outcomes:
measure: Develop a model with high predictive performance for early detection of small cell lung cancer (SCLC) in the eligible patient population.
timeFrame: 11 years
measure: 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.
timeFrame: 11 years
measure: Identify features with the highest discriminatory power of lung cancer prediction and early detection.
timeFrame: 11 years
measure: Identify features with the highest discriminatory power to distinguish between lung cancer patients in stage I-II and stage III-IV.
timeFrame: 11 years
Locations:
city: Golnik
country: Slovenia
facility: University Clinic of Respiratory and Allergic Diseases Golnik
city: Ljubljana
country: Slovenia
facility: Jozef Stefan Institute
Study officials:
References: