Trial Title:
ARtificial Intelligence for Gross Tumour vOlume Segmentation
NCT ID:
NCT05775068
Condition:
Lung Cancer
Conditions: Keywords:
artificial intelligence
deep learning
federated learning
computed tomography
tumor segmentation
radiation dosimetry
treatment planning
Study type:
Observational
Overall status:
Active, not recruiting
Study design:
Time perspective:
Retrospective
Intervention:
Intervention type:
Radiation
Intervention name:
Radiotherapy
Description:
Radiotherapy
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.
Detailed description:
Lung cancer (LC) is the single leading cancer cause of death worldwide (age-standardized
rate of 18.5 per 100,000 population), outstripping the mortality from cancers of the
breast, gastro-intestinal tract and reproductive organs. Radiotherapy (RT), often in
combination with other treatments, has an essential role in managing LC. An essential
step in the RT process is to draw the outline of the Gross Tumor Volume (GTV) in the lung
on axial computed tomography (CT) scans. The step is required for precisely directing
tumoricidal radiation to the target, and simultaneously avoiding irradiation of adjacent
healthy tissue as much as reasonably achievable.
However, tumor outlining by hand consumes a large amount of expert physician time, and
has demonstrably high levels of inter- and intra-observer variability. Part of a clinical
solution would require validated automated systems that work well for complex GTVs in a
wide variety of clinical settings. In recent times, a subclass of artificial intelligence
known as deep learning neural networks (DLNNs) has shown promising potential to assist
clinicians for such image processing tasks. The immense appeal of DLNN-based tools, if
they can be safely shown to add value into radiotherapy clinical workflow, is easily
understandable - these have the potential to significantly boost the productivity of
clinicians by automating a portion of labor-intensive work.
In respect to LC, models trained on selective data from few institutions are the norm.
What the field lacks is not simply large sample size, but sufficient diversity and
heterogeneity of subjects to represent the real world, and the means to train a DLNN on
such a population. That such a population exists among all the RT clinics around the
world is indisputable, however the question is how do we utilize data from all over the
world for such a purpose.
"Federated Learning" very clearly addresses this by side-stepping a few of the
administrative complication of transferring individual-patient level data across national
borders. Federated learning is an implementation of the Personal Health Train (PHT)
paradigm, where we send research questions to each other in the form of software and
exchange anonymous statistical results (such as a DLNN model) instead of sending patient
data around. Hence PHT addresses two of the major challenges of using large-scale cancer
data at a single stroke: (a) using data for a good purpose in spite of the geographic
dispersion of oncology data, and (b) reducing privacy concerns associated sharing of
private patient data across borders.
Objective
Project ARGOS will demonstrate how some of the infrastructural challenges of federated
deep learning and early clinical feasibility barriers to an LC GTV DLNN-based automated
segmentation model might be developed using a PHT approach. ARGOS adopts a global,
cooperative, vendor-agnostic and inter-disciplinary approach to AI development using
decentralized imaging datasets. As our first starting step, we will focus on less complex
clinical cases where the LC primary GTV is mostly contained inside the lung.
ARGOS plans to use existing radiotherapy planning CT delineations from several leading
radiotherapy centres throughout Europe, Asia, Oceania and North America. No new patient
data will be required because all the existing data already resides inside RT clinics as
a result of standard-of-care treatment.
The initial objective will be to train a DLNN that automatically segments the LC primary
GTV that is mostly or entirely contained in the lung parenchyma. The ARGOS partners will
also independently validate the globally-trained model on holdout validation and external
test datasets.
Sub-objectives
1. Share know-how among radiotherapy centres around the world for setting up the
required radiotherapy imaging data and metadata as "FAIR imaging data stations".
2. Offer a vendor-neutral and platform-agnostic open-source architecture for global
federated deep learning ("secure tracks").
3. Provide a registration and credentialing procedure for packaging deep learning
algorithms as a docker container software application ("docker trains").
4. Define a project governance structure and standardized operational principles,
including collaborative research agreements, data protection and intellectual
property valorization.
Criteria for eligibility:
Study pop:
Retrospectively archive/registry-extracted adult lung cancer patients treated with
external beam radiotherapy, having a GTV mass in the lung (not exclusively mediastinal
disease) on radiotherapy planning CT, such that a Primary Lung GTV has been delineated by
a human expert physician (i.e. radiation oncologist).
Sampling method:
Non-Probability Sample
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
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Maastro Clinic
Address:
City:
Maastricht
Zip:
6229ET
Country:
Netherlands
Start date:
July 1, 2021
Completion date:
December 1, 2024
Lead sponsor:
Agency:
Maastricht Radiation Oncology
Agency class:
Other
Collaborator:
Agency:
Universitaire Ziekenhuizen KU Leuven
Agency class:
Other
Collaborator:
Agency:
Radboud University Medical Center
Agency class:
Other
Collaborator:
Agency:
The Netherlands Cancer Institute
Agency class:
Other
Collaborator:
Agency:
University Hospital, Basel, Switzerland
Agency class:
Other
Collaborator:
Agency:
University of Zurich
Agency class:
Other
Collaborator:
Agency:
University Medical Center Groningen
Agency class:
Other
Collaborator:
Agency:
Isala
Agency class:
Other
Collaborator:
Agency:
Tianjin Medical University Cancer Institute and Hospital
Agency class:
Other
Collaborator:
Agency:
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Agency class:
Other
Collaborator:
Agency:
Cardiff University
Agency class:
Other
Collaborator:
Agency:
The Leeds Teaching Hospitals NHS Trust
Agency class:
Other
Collaborator:
Agency:
The Christie NHS Foundation Trust
Agency class:
Other
Collaborator:
Agency:
Cambridge University Hospitals NHS Foundation Trust
Agency class:
Other
Collaborator:
Agency:
Hospital Israelita Albert Einstein
Agency class:
Other
Collaborator:
Agency:
University of Pennsylvania
Agency class:
Other
Collaborator:
Agency:
Liverpool Hospital, South Western Sydney Local Health District
Agency class:
Other
Collaborator:
Agency:
MVR Cancer Centre and Research Institute India
Agency class:
Other
Collaborator:
Agency:
H. Lee Moffitt Cancer Center and Research Institute
Agency class:
Other
Collaborator:
Agency:
Oslo University Hospital
Agency class:
Other
Collaborator:
Agency:
Christian Medical College, Vellore, India
Agency class:
Other
Collaborator:
Agency:
Fudan University
Agency class:
Other
Collaborator:
Agency:
Swiss Institute of Bioinformatics
Agency class:
Other
Collaborator:
Agency:
Guangdong Provincial People's Hospital
Agency class:
Other
Collaborator:
Agency:
National Institute of Technology Calicut
Agency class:
Other
Collaborator:
Agency:
Maastricht University
Agency class:
Other
Source:
Maastricht Radiation Oncology
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05775068
http://www.personalhealthtrain.nl/
