Non Small Cell Lung CancerNCT03564457
20K Distributed Learning Challenge
An observational study of Non Small Cell Lung Cancer, run by Maastricht Radiation Oncology at 1 site in Netherlands.
Trial summary
Machine learn a predictive model from more than 20.000 non-small cell lung cancer patients from more than 5 health care providers from more than 5 countries. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Non Small Cell Lung Cancer
- How many people
- 20,000 participants
- Where
- 1 siteNetherlands
- Study dates
- 1 Jul 2018to 1 Oct 2018
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: All patients with non-small cell lung cancer who have been treated in one of the participating hospitals
Who may be able to join
- Non small cell lung cancer
- Treated in one of the participating hospitals
Who cannot join
- No non small cell lung cancer
- Not treated in one of the participating centers
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Distributed Learning of a Survival Model in More Than 20.000 Lung Cancer Patients
Machine learn a predictive model from more than 20.000 non-small cell lung cancer patients from more than 5 health care providers from more than 5 countries.
Read the detailed description
All current innovations in medicine, including personalized medicine; artificial intelligence; (Big) data driven medicine; learning health care system; value based health care and decision support systems, rely on the sharing of data across health care providers. But sharing of data is hampered by administrative, political, ethical and technical barriers(Sullivan et al., 2011). This limits the amount of health data available for the above innovations and life sciences in general as well as other secondary uses such as quality improvement.
The investigators hypothesize that sharing questions rather than sharing data is a better approach and can unlock orders of magnitude more data while limiting privacy and other concerns. An infrastructure to bring questions to the data has been demonstrated to work recently in project such as euroCAT(Lambin et al., 2013; Deist et al., 2017), Datashield (Gaye et al., 2014) and OHDSI (Hripcsak et al., 2015). However, the scale of the prior work has been limited in terms of the number of data subjects, number of data providers and global coverage.
In the experience of the investigators, the main challenges of scaling up the infrastructure are 1) the effort necessary to make data FAIR at each site ("stations"), 2) the technical and legal governance ("track") and 3) the mathematics and engineering of learning applications ("trains") - together called the Personal Health Train (PHT) infrastructure. Since multiple years a global consortium of healthcare providers, scientists and commercial parties called CORAL (Community in Oncology for RApid Learning) have worked on all three PHT challenges.
The aim of this study is to show that the PHT distributed learning infrastructure can be scaled to many 1000s of patients, specifically the investigators aim to machine learn a predictive model from more than 20.000 non-small cell lung cancer patients from more than 5 health care providers from more than 5 countries.
- Conditions
- Non Small Cell Lung Cancer
- Being tested
- No interventions will take place (observational)
- Medical subject headings
- Carcinoma, Non-Small-Cell Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventions will take place (observational)Other
No interventions will take place (observational)
| Study group | Role | What they receive |
|---|---|---|
| One group of 20.000 patients | No interventions will take place as this is an observational study Receives: Other: No interventions will take place (observational) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall survival
Overall survival
Measured: 2 years after (any) treatment for non small cell lung cancer
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Jun 2018
- Study start1 Jul 2018
- Primary completion1 Oct 2018
- Study completion1 Oct 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Non Small Cell Lung CancerStudies 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 20,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Maastricht Radiation Oncology
- Sponsor type
- University, hospital or other
- Collaborators
- Radboud University Medical Center, The Netherlands Cancer Institute, Manchester Academic Health Science Centre, Catholic University of the Sacred Heart, Fudan University, Velindre Cancer Center, University of Michigan, Cardiff University
- Responsible party
- Sponsor
- Investigators
- André Dekker, MD, PhD, Principal investigator, Maastro Clinic, The Netherlands
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03564457
- Sponsor's protocol ID
- 20K Distributed Learning
- Status last verified
- Mar 2019
- First submitted
- 11 Jun 2018
- Last updated
- 8 Mar 2019
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Mar 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Distributed Learning of a Survival Model in More Than 20.000 Lung Cancer Patients
- ClinicalTrials.gov ID
- NCT03564457
- Acronym / sponsor ID
- 20K Distributed Learning
- Conditions
- Non Small Cell Lung Cancer
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 20,000 participants (actual)
- Lead sponsor
- Maastricht Radiation OncologyUniversity, hospital or other
- Collaborators
- Radboud University Medical Center, The Netherlands Cancer Institute, Manchester Academic Health Science Centre, Catholic University of the Sacred Heart, Fudan University, Velindre Cancer Center, University of Michigan, Cardiff University
- Study start
- 1 Jul 2018 (actual)
- Primary completion
- 1 Oct 2018 (actual)
- Study completion
- 1 Oct 2018 (actual)
- First posted
- 20 Jun 2018 (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.
Machine learn a predictive model from more than 20.000 non-small cell lung cancer patients from more than 5 health care providers from more than 5 countries.
Detailed description
All current innovations in medicine, including personalized medicine; artificial intelligence; (Big) data driven medicine; learning health care system; value based health care and decision support systems, rely on the sharing of data across health care providers. But sharing of data is hampered by administrative, political, ethical and technical barriers(Sullivan et al., 2011). This limits the amount of health data available for the above innovations and life sciences in general as well as other secondary uses such as quality improvement.
The investigators hypothesize that sharing questions rather than sharing data is a better approach and can unlock orders of magnitude more data while limiting privacy and other concerns. An infrastructure to bring questions to the data has been demonstrated to work recently in project such as euroCAT(Lambin et al., 2013; Deist et al., 2017), Datashield (Gaye et al., 2014) and OHDSI (Hripcsak et al., 2015). However, the scale of the prior work has been limited in terms of the number of data subjects, number of data providers and global coverage.
In the experience of the investigators, the main challenges of scaling up the infrastructure are 1) the effort necessary to make data FAIR at each site ("stations"), 2) the technical and legal governance ("track") and 3) the mathematics and engineering of learning applications ("trains") - together called the Personal Health Train (PHT) infrastructure. Since multiple years a global consortium of healthcare providers, scientists and commercial parties called CORAL (Community in Oncology for RApid Learning) have worked on all three PHT challenges.
The aim of this study is to show that the PHT distributed learning infrastructure can be scaled to many 1000s of patients, specifically the investigators aim to machine learn a predictive model from more than 20.000 non-small cell lung cancer patients from more than 5 health care providers from more than 5 countries.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| One group of 20.000 patients | – | Other: No interventions will take place (observational)No interventions will take place as this is an observational study |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- All patients with non-small cell lung cancer who have been treated in one of the participating hospitals
Key inclusion criteria (2 listed)
- Non small cell lung cancer
- Treated in one of the participating hospitals
Key exclusion criteria (2 listed)
- No non small cell lung cancer
- Not treated in one of the participating centers
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 |
|---|---|---|
| No interventions will take place (observational)No interventions will take place (observational) | Other | One group of 20.000 patients |
Primary outcome
Overall survival
Time frame: 2 years after (any) treatment for non small cell lung cancer
Overall survival
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Mar 2019.
Related trials for Non Small Cell Lung Cancer
Active, not recruitingPhase 1/2
A Beta-only IL-2 ImmunoTherapY Study
Advanced Solid Tumor, Unresectable Solid Tumor
RecruitingPhase 2/3
Phase 2 Trial of Adagrasib Monotherapy and in Combination With Pembrolizumab and a Phase 3 Trial of Adagrasib in Combination in Patients With a KRAS G12C Mutation KRYSTAL-7
Advanced Non-Small Cell Lung Cancer, Metastatic Non-Small Cell Lung Cancer
RecruitingPhase 3
A Clinical Trial of Adjuvant Intismeran (V940) With or Without Pembrolizumab Coformulated With Berahyaluronidase Alfa (MK-3475A) in High-Risk Stage I Non-Small Cell Lung Cancer (V940-014)
Non-small Cell Lung Cancer
Recruiting
Understanding the Feasibility of a Co-designed Pre-radiotherapy Exercise Programme - A Single-arm Study
Lung Cancer
RecruitingPhase 2
Adjuvant ctDNA-Adapted Personalized Treatment in Early Stage NSCLC (ADAPT-E)
Non-small Cell Lung Cancer, Non-small Cell Lung Cancer Stage I
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.
André Dekker, MD, PhD principal investigator
Maastro Clinic, The Netherlands
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Non Small Cell Lung Cancer, run by Maastricht Radiation Oncology at 1 site in Netherlands.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. 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: finished, as of the sponsor's last update on 8 Mar 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Netherlands. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall survival (time frame: 2 years after (any) treatment for non small cell lung cancer). 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 8 Mar 2019.