ARtificial Intelligence for Gross Tumour vOlume Segmentation
Trial ID: NCT05775068
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-07-01
Completion date: 2024-12-01
Sponsor: Maastricht Radiation Oncology
Collaborators: Universitaire Ziekenhuizen KU Leuven; Radboud University Medical Center; The Netherlands Cancer Institute; University Hospital, Basel, Switzerland; University of Zurich; University Medical Center Groningen; Isala; Tianjin Medical University Cancer Institute and Hospital; Fondazione Policlinico Universitario Agostino Gemelli IRCCS; Cardiff University; The Leeds Teaching Hospitals NHS Trust; The Christie NHS Foundation Trust; Cambridge University Hospitals NHS Foundation Trust; Hospital Israelita Albert Einstein; University of Pennsylvania; Liverpool Hospital, South Western Sydney Local Health District; MVR Cancer Centre and Research Institute India; H. Lee Moffitt Cancer Center and Research Institute; Oslo University Hospital; Christian Medical College, Vellore, India; Fudan University; Swiss Institute of Bioinformatics; Guangdong Provincial People's Hospital; National Institute of Technology Calicut; Maastricht University
Conditions: Lung Cancer
Interventions: Radiotherapy
Locations: Maastro Clinic
Countries: Netherlands
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

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.

Eligibility criteria:
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

Outcomes:
As-treated primary GTV delineation in lung