Adenocarcinoma of Lung; Bronchial Neoplasms; Early Detection of Cancer; Machine LearningNCT05907577
Early Detection of Lung Cancer With Machine Learning Based on Routine Clinical Investigations
An observational study of Adenocarcinoma of Lung; Bronchial Neoplasms; Early Detection of Cancer; Machine Learning, run by The University Clinic of Pulmonary and Allergic Diseases Golnik at 2 sites in Slovenia.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 to 79 yearsHealthy volunteers can take part
- Conditions the study is for
- Adenocarcinoma of Lung; Bronchial Neoplasms; Early Detection of Cancer; Machine Learning
- What is being tested?
- Observational
- How many people
- 7,500 participants
- Where
- 2 sitesSlovenia
- Study dates
- 1 Sep 2023to 1 Sep 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 to 79 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
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.
Trial locations and contacts
2 sites in 1 country.
Slovenia2 sites
University Clinic of Respiratory and Allergic Diseases Golnik
Golnik, 4204
Ales Rozman, MD, PhD, ales.rozman@klinika-golnik.si
Mitja Prah, MD
Jozef Stefan Institute
Ljubljana, 1000
Mitja Luštrek, PhD, mitja.lustrek@ijs.si
Mitja Luštrek, PhD
About this study
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.
- Being tested
- Observational
- Medical subject headings
- Adenocarcinoma of Lung, Bronchial Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ObservationalOther
No interventions.
| Study group | Role | What they receive |
|---|---|---|
| Disease cohort | Observational, no interventions Receives: Other: Observational | |
| Control cohort | Observational, no interventions Receives: Other: Observational |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 11 years
Secondary outcomes (1)
Demonstrate that the newly developed model achieves higher prediction accuracy than the well-validated model PLCOm2012.
Measured: 11 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jun 2023
- Study start1 Sep 2023 (estimated)
- Primary completion1 Sep 2024 (estimated)
- Study completion1 Sep 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Adenocarcinoma of Lung; Bronchial Neoplasms; Early Detection of Cancer; Machine LearningStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 7,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The University Clinic of Pulmonary and Allergic Diseases Golnik
- Sponsor type
- University, hospital or other
- Collaborators
- Jozef Stefan Institute
- Responsible party
- Principal investigator, Aleš Rozman, The University Clinic of Pulmonary and Allergic Diseases Golnik
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05907577
- Sponsor's protocol ID
- 3023226
- Status last verified
- Jun 2023
- First submitted
- 8 Jun 2023
- Last updated
- 18 Jun 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Jun 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Early Detection of Lung Cancer With Machine Learning Based on Routine Clinical Investigations
- ClinicalTrials.gov ID
- NCT05907577
- Acronym / sponsor ID
- 3023226
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 7,500 participants (estimated)
- Lead sponsor
- The University Clinic of Pulmonary and Allergic Diseases GolnikUniversity, hospital or other
- Collaborators
- Jozef Stefan Institute
- Study start
- 1 Sep 2023 (estimated)
- Primary completion
- 1 Sep 2024 (estimated)
- Study completion
- 1 Sep 2024 (estimated)
- First posted
- 18 Jun 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Disease cohort | – | Other: ObservationalObservational, no interventions |
| Control cohort | – | Other: ObservationalObservational, no interventions |
Who may qualify
- Ages
- 50 to 79 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| ObservationalNo interventions. | Other | Control cohort, Disease cohort |
Primary outcome
Develop a model with high predictive performance for early detection of non-small cell lung cancer (NSCLC) in the eligible patient population.
Time frame: 11 years
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.
1 secondary outcome
Demonstrate that the newly developed model achieves higher prediction accuracy than the well-validated model PLCOm2012.
Time frame: 11 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Jun 2023.
- Individual participant dataSharing planned: No
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
University Clinic of Respiratory and Allergic Diseases Golnik
Golnik, Slovenia
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Adenocarcinoma of Lung; Bronchial Neoplasms; Early Detection of Cancer; Machine Learning, run by The University Clinic of Pulmonary and Allergic Diseases Golnik at 2 sites in Slovenia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 18 Jun 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Slovenia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Develop a model with high predictive performance for early detection of non-small cell lung cancer (NSCLC) in the eligible patient population. (time frame: 11 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Jun 2023.