Clinical trial · Observational
ARtificial Intelligence for Gross Tumour vOlume Segmentation
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Radiotherapy | Radiation | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- As-treated primary GTV delineation in lung
- timeFrame
- Before radiotherapy
- description
- Gross Tumor Volume as delineated by a medical professional on a treatment planning computed tomography scan for the purpose of radiation planning/dosimetry but not re-drawn/re-edited for this research study.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Primary lung cancer, either small-cell or non-small cell * Any stage of primary disease * Radiotherapy planning Computed Tomography (CT) series taken before the commencement of radiotherapy * Gross Tumor Volume delineated (see primary outcome above) * CT series in DICOM format * Primary GTV delineation (not including respiratory motion) in RT-Structure DICOM format for one matching CT series * Any type of external beam radiotherapy treatment received * Combinations with other therapies permitted Exclusion Criteria: * Not a primary in the lung * Exclusively nodal disease in mediastinum with no visible hyperintense mass within the outlines of the lung parenchyma * Only has CT series taken after lung resection * CT reconstructed pixel spacing (spatial resolution) exceeding 1.1 mm per pixel * CT reconstructed slice thickness is greater than 3 mm per slice
References
Publications (1)
- DERIVEDChoudhury A, Volmer L, Martin F, Fijten R, Wee L, Dekker A, Soest JV. Advancing Privacy-Preserving Health Care Analytics and Implementation of the Personal Health Train: Federated Deep Learning Study. JMIR AI. 2025 Feb 6;4:e60847. doi: 10.2196/60847. PMID 39912580