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Life sciences · Observational Study
ClinicalTrials.gov · October 15, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Observational Study.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
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Registry record from ClinicalTrials.gov (NCT05775068). This is a study registration, not published results. Lead sponsor: Maastricht Radiation Oncology. Recruitment status: ACTIVE_NOT_RECRUITING. Study type: OBSERVATIONAL. Enrollment: 2000 participants (ESTIMATED). Conditions: Lung Cancer. Interventions: RADIATION: Radiotherapy. Primary outcome measures: As-treated primary GTV delineation in lung , Before radiotherapy. Brief summary: Identifying the outline of a Gross Tumour Volume (GTV) in lung cancer is an essential step in radiation treatment. Clinical research, such as radiomics and image-based prognostication, requires the GTV to be pre-defined on massive imaging datasets. The ARGOS community creates an open-source and vendor-agnostic federated learning infrastructure that makes it possible to train a deep learning neural network to automatically segment Lung Cancer GTV on computed tomography images. To reduce risks associated with sharing of patient data, we have used a data-secure Federated Learning paradigm known as the "Personal Health Train" that has been jointly developed by MAASTRO Clinic and the Dutch Comprehensive Cancer Organization (IKNL). The successful completion of this project will deliver a highly scalable and readily-reusable framework where multiple clinics anywhere in the world - large or small - can equitably collaborate and solve complex clinical problems with the help of artificial intelligence and massive amounts of data, while reducing the barriers associated with moving sensitive patient data across borders.