20K Distributed Learning Challenge

Non Small Cell Lung Cancer Clinical Trial

Official title:

Distributed Learning of a Survival Model in More Than 20.000 Lung Cancer Patients

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03564457
• Other study ID #: 20K Distributed Learning
• Status: Completed
• Start date: July 1, 2018
• Est. completion date: October 1, 2018

Study information

• Verified date: March 2019
• Source: Maastricht Radiation Oncology
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 20000
• Est. completion date: October 1, 2018
• Est. primary completion date: October 1, 2018
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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

Related Conditions & MeSH terms

• Carcinoma, Non-Small-Cell Lung
• Lung Neoplasms
• Non Small Cell Lung Cancer

Intervention

Other:
• No interventions will take place (observational)
No interventions will take place (observational)

Locations

Country	Name	City	State
Netherlands	MAASTRO clinic	Maastricht

Sponsors (9)

Lead Sponsor	Collaborator
Maastricht Radiation Oncology	Cardiff University, Catholic University of the Sacred Heart, Fudan University, Manchester Academic Health Science Centre, Radboud University, The Netherlands Cancer Institute, University of Michigan, Velindre Cancer Center

Country where clinical trial is conducted

Netherlands, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Overall survival	Overall survival	2 years after (any) treatment for non small cell lung cancer