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Primeur weekly 2018-05-14

Crowd computing

Supercomputing power for rainfall modelling in Africa ...

Quantum computing

D-Wave announces Quadrant machine learning business unit ...

MDR Corporation and D-Wave Systems announce quantum computing agreement ...

Focus on Europe

PPI4HPC starts its joint procurement process ...

BSC awarded ESA project to evaluate low-power GPUs for space applications ...

GCS begins next-generation architecture transition and approves more than 1 billion computing core hours for large-scale simulation projects ...

Middleware

ClusterVision awarded contract to deliver Scandinavia's most powerful supercomputer ...

TIBCO and Amazon Web Services break performance record ...

Towards sustainable blockchains ...

Hardware

Intersect360 Research invites to participate in annual HPC Site Census study ...

SDSC's Industry Partners Programme announces Technology Forum roundtables ...

Applications

Excellence in science drives PRACE 16th Call for Project Access ...

Who will win the Dutch Data Prize 2018? ...

Waterloo chemists create faster and more efficient way to process information ...

Montana State student wins NSF fellowship to advance research on fluid sprays ...

Montana State researcher wins NSF CAREER award ...

An AI oncologist to help cancer patients worldwide ...

The Cloud

Oracle delivers next set of autonomous Cloud platform services ...

Mellanox Technologies selects Univa to extend silicon design HPC cluster to hybrid Cloud ...

Mellanox and Red Hat deliver enhanced performance and simplicity for NFV infrastructure and Agile Cloud data centres ...

IBM and Red Hat join forces to accelerate hybrid Cloud adoption ...

Red Hat and Microsoft co-develop the first Red Hat OpenShift jointly managed service on a public Cloud ...

An AI oncologist to help cancer patients worldwide

Comparison between predicted ground-truth clinical target volume (CTV1) (blue) and physician manual contours (red) for four oropharyngeal cancer patients. The primary and nodal gross tumour volume is included (green). From left to right, we illustrate a case from each site and nodal status (base of tongue node-negative, tonsil node-negative, base of tongue node-positive, and tonsil node-positive). Credit: Carlos E. Cardenas, MD Anderson Cancer Center.9 May 2018 Austin - Before performing radiation therapy, oncologists review medical images to identify tumours and surrounding tissue, a process known as contouring. Researchers from MD Anderson Cancer Center developed a new method for automating the contouring of high-risk clinical target volumes using artificial intelligence and supercomputers. They found the predicted contours could be implemented clinically, with only minor or no changes.

Known as contouring, this process establishes how much radiation a patient will receive and how it will be delivered. In the case of head and neck cancer, this is a particularly sensitive task due to the presence of vulnerable tissues in the vicinity.

Though it may sound straightforward, contouring clinical target volumes is quite subjective. A recent study from Utrecht University found wide variability in how trained physicians contoured the same patient's computed tomography (CT) scan, leading some doctors to suggest high-risk clinical target volumes eight times larger than their colleagues.

This inter-physician variability is a problem for patients, who may be over- or under-dosed based on the doctor they work with. It is also a problem for determining best practices, so standards of care can emerge.

Recently, Carlos Cardenas, a graduate research assistant and PhD candidate at the University of Texas MD Anderson Cancer Center in Houston, Texas, and a team of researchers at MD Anderson, working under the supervision of Laurence Court with support from the National Institutes of Health, developed a new method for automating the contouring of high-risk clinical target volumes using artificial intelligence and deep neural networks.

They report their results in the June 2018 issue of the International Journal of Radiation Oncology*Biology*Physics .

Carlos Cardenas' work focuses on translating a physician's decision-making process into a computer program. "We have a lot of clinical data and radiation therapy treatment plan data at MD Anderson", he stated. "If we think about the problem in a smart way, we can replicate the patterns that our physicians are using to treat specific types of tumours."

In their study, they analyzed data from 52 oropharyngeal cancer patients who had been treated at MD Anderson between January 2006 to August 2010, and had previously had their gross tumour volumes and clinical tumour volumes contoured for their radiation therapy treatment.

Carlos Cardenas spent a lot of time observing the radiation oncology team at MD Anderson, which has one of the few teams of head and neck subspecialist oncologists in the world, trying to determine how they define the targets.

"For high-risk target volumes, a lot of times radiation oncologists use the existing gross tumor disease and apply a non-uniform distance margin based on the shape of the tumor and its adjacent tissues", Carlos Cardenas stated. "We started by investigating this first, using simple distance vectors."

Carlos Cardenas began the project in 2015 and had quickly accumulated an unwieldy amount of data to analyze. He turned to deep learning as a way of mining that data and uncovering the unwritten rules guiding the experts' decisions.

The deep learning algorithm he developed uses auto-encoders - a form of neural networks that can learn how to represent datasets - to identify and recreate physician contouring patterns.

The model uses the gross tumor volume and distance map information from surrounding anatomic structures as its inputs. It then classifies the data to identify voxels - three-dimensional pixels - that are part of the high-risk clinical target volumes. In oropharyngeal cancer cases, the head and neck are usually treated with different volumes for high, low and intermediate risk. The paper described automating the target for the high-risk areas. Additional forthcoming papers will describe the low and intermediate predictions.

Carlos Cardenas and his collaborators tested the method on a subset of cases that had been left out of the training data. They found that their results were comparable to the work of trained oncologists. The predicted contours agreed closely with the ground-truth and could be implemented clinically, with only minor or no changes.

In addition to potentially reducing inter-physician variability and allowing comparisons of outcomes in clinical trials, a tertiary advantage of the method is the speed and efficiency it offers. It takes a radiation oncologist two to four hours to determine clinical target volumes. At MD Anderson, this result is then peer reviewed by additional physicians to minimize the risk of missing the disease.

Using the Maverick supercomputer at the Texas Advanced Computing Center (TACC), they were able to produce clinical target volumes in under a minute. Training the system took the longest amount of time, but for that step too, TACC resources helped speed up the research significantly.

"If we were to do it on our local GPU [graphics processing unit], it would have taken two months", Carlos Cardenas stated. "But we were able to parallelize the process and do the optimization on each patient by sending those paths to TACC and that's where we found a lot of advantages by using the TACC system."

"In recent years, we have seen an explosion of new projects using deep learning on TACC systems", stated Joe Allen, a Research Associate at TACC. "It is exciting and fulfilling for us to be able to support Carlos's research, which is so closely tied to real medical care."

The project is specifically intended to help low-and-middle income countries where expertise in contouring is rarer, although it is likely that the tools will also be useful in the U.S.

Carlos Cardenas said such a tool could also greatly benefit clinical trials by allowing one to more easily compare the outcomes of patients treated at two different institutions.

Speaking about the integration of deep learning into cancer care, he stated: "I think it's going to change our field. Some of these recommender systems are getting to be very good and we're starting to see systems that can make predictions with a higher accuracy than some radiologists can. I hope that the clinical translation of these tools provides physicians with additional information that can lead to better patient treatments."

Source: University of Texas at Austin, Texas Advanced Computing Center - TACC

Back to Table of contents

Primeur weekly 2018-05-14

Crowd computing

Supercomputing power for rainfall modelling in Africa ...

Quantum computing

D-Wave announces Quadrant machine learning business unit ...

MDR Corporation and D-Wave Systems announce quantum computing agreement ...

Focus on Europe

PPI4HPC starts its joint procurement process ...

BSC awarded ESA project to evaluate low-power GPUs for space applications ...

GCS begins next-generation architecture transition and approves more than 1 billion computing core hours for large-scale simulation projects ...

Middleware

ClusterVision awarded contract to deliver Scandinavia's most powerful supercomputer ...

TIBCO and Amazon Web Services break performance record ...

Towards sustainable blockchains ...

Hardware

Intersect360 Research invites to participate in annual HPC Site Census study ...

SDSC's Industry Partners Programme announces Technology Forum roundtables ...

Applications

Excellence in science drives PRACE 16th Call for Project Access ...

Who will win the Dutch Data Prize 2018? ...

Waterloo chemists create faster and more efficient way to process information ...

Montana State student wins NSF fellowship to advance research on fluid sprays ...

Montana State researcher wins NSF CAREER award ...

An AI oncologist to help cancer patients worldwide ...

The Cloud

Oracle delivers next set of autonomous Cloud platform services ...

Mellanox Technologies selects Univa to extend silicon design HPC cluster to hybrid Cloud ...

Mellanox and Red Hat deliver enhanced performance and simplicity for NFV infrastructure and Agile Cloud data centres ...

IBM and Red Hat join forces to accelerate hybrid Cloud adoption ...

Red Hat and Microsoft co-develop the first Red Hat OpenShift jointly managed service on a public Cloud ...