ARtificial Intelligence for Gross Tumour vOlume Segmentation

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.

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.

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