RaySearch Annual Review 2020

DATA-DRIVEN ONCOLOGY

The commercial launch of RayIntelligence® heralds a new era of cloud-based, data-driven oncology. "In every respect, a collective achievement from a group of engineers and scientists who combine deep domain knowledge with a passion for collaboration and a laser focus on the customer’s needs."

– Fredrik Löfman, Head of Machine Learning at RaySearch

MACHINE LEARNING: SPEED, AUTOMATION, EFFICIENCY

The clinical roll-out of machine learning technologies represents an inflection point in radiation oncology, promising innovation and transformation – at scale – across the planning, delivery and management of cancer treatment programs. For Fredrik Löfman, Head of RaySearch’s machine learning group, the operational upsides for oncology clinics are compelling – and already within reach.

“Machine learning is all about increased efficiency, consistency and automation of the core clinical workflows in radiation therapy,” explains Löfman. That’s a broad canvas: think tumor and organ segmentation, optimized treatment planning, and the end-game of online adaptive radiotherapy tailored to the unique needs of each patient. “Our portfolio of pretrained machine learning models has scaled significantly since January,” he notes, “while the rest of this year is all about the commercial introduction of these models, accelerating uptake and gaining further clinical validation from early-adopters.”

That process, it seems, is well under way. Consider the usecase at Mälarsjukhuset in Eskilstuna, Sweden. In spring of last year, against a backdrop of COVID-induced staff shortages, Mälarsjukhuset’s medical physics team fasttracked clinical deployment of RayStation’s deep-learning segmentation algorithms – a move that reduced the time spent on patient contouring for planning of pelvic and prostate radiotherapy by as much as 75% versus manual or semi-automatic methods. Put simply, RayStation’s deep-learning functionality* – trained and validated on a cohort of around 350 prior clinical cases – automatically creates contours of the patient’s organs at risk in the tumor near-environment. Clinical staff at Mälarsjukhuset are then able to review and, as necessary, fine-tune the segmentation, such that contouring now only takes 10-15 minutes per patient (versus up to an hour previously).

* Subject to regulatory clearance in some markets.

“The Eskilstuna team was incredibly efficient,” says Löfman, “ implementing our deep-learning algorithms in a secure and efficient way to realize immediate operational efficiencies.” What’s more, RaySearch’s newly released head-and-neck model, developed and trained in collaboration with researchers at the University of Turin, promises even bigger time savings, with automatic contouring of 35 critical structures (versus just five for the pelvic model). “Over time, we aim to cover all the main disease sites for radiotherapy treatment,” notes Löfman. “We have the methodology, the algorithms and the infrastructure within RayStation®. In the 2021 release schedule, for example, we will introduce pretrained deep-learning models for autosegmentation of structures in the breast region (including lymph nodes) as well as other structures relevant in radiotherapy planning.”

MODEL BEHAVIOURS Alongside clinical “go-live” of deep-learning segmentation, Löfman and his cross-disciplinary team – 20 scientists and engineers split across planning, imaging and analytics subgroups – have been preparing the ground for a step-function expansion of machine learning-enabled automation in treatment planning. Here, RaySearch’s machine learning models are used to predict and optimize 3D spatial dose, with in-built strategies to automatically generate a set of deliverable treatment plan candidates. The result: fast-track comparison of treatment plan options followed by selection of the optimal plan for each patient in terms of tumor coverage, conformality and tissue sparing. “Unlike traditional manual planning, the radiation oncology team will start the day with, say, five candidate plans for each patient scheduled to receive treatment,” explains Löfman. “They’ll quickly review those plans, pick the most suitable, and then either fine-tune or approve that plan straight away.”

A long-standing R&D collaboration with the Princess Margaret Cancer Centre in Toronto will see nine validated and pretrained machine learning models included as part of the RayStation 11A* release (with at least 10 more pretrained models to follow in the 11B* release next year). Example sites include prostate, breast, head-and-neck, liver, rectum, brain and lung – most of them incorporating

different stages and treatment protocols. Worth noting as well that RayStation 11A will include RaySearch’s first pretrained machine learning model for proton therapy planning (another head-and-neck model).

“Clinics can tune and commission these models on their own local data and treatment protocols,” explains Löfman. “They can also share models without sharing their patient data.” All of which streamlines the path to clinical translation, with users able to “plug-and-play” rather than the far more onerous task of collecting data and then training their own models.

MODEL COLLABORATIONS Down the line, ambitious plans are taking shape for a RaySearch-hosted central repository to promote sharing and co-development of machine learning models across the radiation oncology community. The vision: an interactive hub to facilitate multicenter collaboration, promote best practice and accelerate clinical innovation on machine learning treatment protocols. The specifics: think quality assurance and benchmarking of models; version handling and regression testing; as well as monitoring of model distribution. “There are significant opportunities arising from colocation,” says Löfman. “A unified repository will allow cancer centers to upload or download a diverse portfolio of machine learning models – evaluating, comparing and benchmarking them for research or clinical purposes.”

Taking the long view, it’s data – and lots of it – that will accelerate machine learning innovation in the radiation oncology workflow – and ultimately the realization of optimized treatments and enhanced patient outcomes. “In order to train and validate machine learning models that can be distributed across borders and across continents,” notes Löfman, “it’s going to be vital to build and share large-scale data sets that researchers, clinics and industry can access in an unbiased and representative way.” The future’s bright, Löfman concludes. “Underpinned by cloud-based analytics platforms like RayIntelligence, a new era of data-driven decisionmaking will redefine what’s possible in comprehensive cancer care."