20K Distributed Learning Challenge

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.

Study Overview

• Status: Completed
• Conditions: Non Small Cell Lung Cancer
• Intervention / Treatment: Other: No interventions will take place (observational)

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 Type: Observational
• Enrollment (Actual): 20000

Contacts and Locations

Study Locations

• Netherlands
  • Maastricht, Netherlands, 6229 ET
    • Maastro clinic

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• 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

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
One group of 20.000 patients; No interventions will take place as this is an observational study	Other: No interventions will take place (observational); No interventions will take place (observational)

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Overall survival	Overall survival	2 years after (any) treatment for non small cell lung cancer

Collaborators and Investigators

• Sponsor: Maastricht Radiation Oncology
• Collaborators: Cardiff University; Radboud University Medical Center; Fudan University; University of Michigan; Catholic University of the Sacred Heart; The Netherlands Cancer Institute; Manchester Academic Health Science Centre; Velindre Cancer Center
• Investigators: Principal Investigator: André Dekker, MD, PhD, Maastro Clinic, The Netherlands

Study record dates

Study Major Dates

• Study Start (Actual): July 1, 2018
• Primary Completion (Actual): October 1, 2018
• Study Completion (Actual): October 1, 2018

Study Registration Dates

• First Submitted: June 11, 2018
• First Submitted That Met QC Criteria: June 11, 2018
• First Posted (Actual): June 20, 2018

Study Record Updates

• Last Update Posted (Actual): March 8, 2019
• Last Update Submitted That Met QC Criteria: March 7, 2019
• Last Verified: March 1, 2019

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: 20K Distributed Learning

Drug and device information, study documents

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