---
title: 20K Distributed Learning Challenge
nct_id: NCT03564457
overall_status: COMPLETED
sponsor: Maastricht Radiation Oncology
study_type: OBSERVATIONAL
primary_condition: Non Small Cell Lung Cancer
countries: Netherlands
canonical_url: "https://parkinsonspathways.com/agent/trials/NCT03564457.md"
clinicaltrials_gov: "https://clinicaltrials.gov/study/NCT03564457"
ct_last_update_post_date: 2019-03-08
last_seen_at: "2026-05-12T06:28:43.285Z"
source: ClinicalTrials.gov (mirrored, no enrichment)
---
# 20K Distributed Learning Challenge

**Official Title:** Distributed Learning of a Survival Model in More Than 20.000 Lung Cancer Patients

**NCT ID:** [NCT03564457](https://clinicaltrials.gov/study/NCT03564457)

## Key Facts

- **Status:** COMPLETED
- **Study Type:** OBSERVATIONAL
- **Target Enrollment:** 20000
- **Lead Sponsor:** Maastricht Radiation Oncology
- **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
- **Conditions:** Non Small Cell Lung Cancer
- **Start Date:** 2018-07-01
- **Completion Date:** 2018-10-01
- **CT.gov Last Update:** 2019-03-08

## 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

- **Sex:** ALL
- **Healthy Volunteers:** No

```
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
```

## Arms

- **One group of 20.000 patients** — No interventions will take place as this is an observational study

## Interventions

- **No interventions will take place (observational)** (OTHER) — No interventions will take place (observational)

## Primary Outcomes

- **Overall survival** _(time frame: 2 years after (any) treatment for non small cell lung cancer)_ — Overall survival

## Locations (1)

- MAASTRO clinic, Maastricht, Netherlands

## Recent Field Changes (last 30 days)

- `outcomes.primary` — added _(2026-05-12)_
- `armsInterventions.arms` — added _(2026-05-12)_
- `armsInterventions.interventions` — added _(2026-05-12)_
- `sponsor.lead` — added _(2026-05-12)_
- `status.overallStatus` — added _(2026-05-12)_
- `status.primaryCompletionDate` — added _(2026-05-12)_
- `status.completionDate` — added _(2026-05-12)_
- `status.lastUpdatePostDate` — added _(2026-05-12)_
- `design.enrollmentCount` — added _(2026-05-12)_
- `eligibility.criteria` — added _(2026-05-12)_
- `eligibility.sex` — added _(2026-05-12)_
- `sponsor.collaborators` — added _(2026-05-12)_
- `results.hasResults` — added _(2026-05-12)_
- `locations.maastro clinic|maastricht||netherlands` — added _(2026-05-12)_

---

*Canonical: https://parkinsonspathways.com/agent/trials/NCT03564457.md*  
*Source data (authoritative): https://clinicaltrials.gov/study/NCT03564457*  
*This page is a raw mirror with no AI summary, no editorial enrichment, and no Parkinson's-specific filtering.*