Clinical trial NCT03564457: 20K Distributed Learning Challenge
Official title: Distributed Learning of a Survival Model in More Than 20.000 Lung Cancer Patients
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Non Small Cell Lung Cancer
Lead sponsor: Maastricht Radiation Oncology
Sponsor class: OTHER
Collaborators: name: Radboud University Medical Center
name: The Netherlands Cancer Institute
name: Manchester Academic Health Science Centre
name: Catholic University of the Sacred Heart
name: Fudan University
name: Velindre Cancer Center
name: University of Michigan
name: Cardiff University
First posted: 2018-06-20
Enrollment: 20000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All patients with non-small cell lung cancer who have been treated in one of the participating hospitals

Brief 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.

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.

Eligibility criteria: Inclusion Criteria:

* Non small cell lung cancer
* Treated in one of the participating hospitals

Exclusion Criteria:

* No non small cell lung cancer
* Not treated in one of the participating centers

Primary outcomes:
measure: Overall survival
timeFrame: 2 years after (any) treatment for non small cell lung cancer
Secondary outcomes:
Other outcomes:
Locations:
city: Maastricht
country: Netherlands
facility: MAASTRO clinic
Study officials:
affiliation: Maastro Clinic, The Netherlands
name: André Dekker, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: