Ignite Creation Date:
2024-05-06 @ 6:45 PM
Last Modification Date:
2024-10-26 @ 2:54 PM
Study NCT ID:
NCT05775068
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-03-27
First Post:
2023-03-07
Brief Title:
ARtificial Intelligence for Gross Tumour vOlume Segmentation
Sponsor:
Maastricht Radiation Oncology
Organization:
Maastricht Radiation Oncology
Study Overview
Official Title:
ARtificial Intelligence for Gross Tumour vOlume Segmentation
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
ARGOS
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
Detailed Description:
Lung cancer LC is the single leading cancer cause of death worldwide age-standardized rate of 185 per 100000 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
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None