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Advances in computer vision combined with AI are helping pathologists to more accurately identify subtypes of cancer - leading to better treatments for patients, according to Dan Ruderman
Looking at cancer
‘Eighty years on, pathologists still rely on their eyes to diagnose,’ explains Dan Ruderman, assistant professor of research medicine at the Keck School of Medicine of USC and the Lawrence J Ellison Institute for Transformative Medicine. ‘Pathologists use their eyes to make
these decisions so whether they have gone from microscope to now looking at digital images that have been scanned in from slides, they are still looking at them with their eyes and making those decisions,’ he continues. Ruderman and his colleagues at the
Ellison Institute have been using large- scale AI simulations run in partnership with Oracle using the Oracle Cloud Infrastructure. Their research looks to take pre-existing Hematoxylin and eosin stain (H&E stains) and using them to identify cancer subtypes by training neural networks. ‘Every patient around the world who goes through surgery and has a pathologist examine specimens has an H&E stain done to find out what the diagnosis of this patient is,’ states Ruderman. ‘This means that it costs zero dollars because the test has already been done.’
Computer assisted diagnosis ‘What is surprising about that is, given the amazing advances we have had in computer vision for things like self- driving cars and certain modalities in medical imaging, that we are not using the advanced capabilities of computers to help in these diagnoses to make better decisions for patient care.’ Today the fields of visual pathology and AI and machine learning are being employed to solve particularly challenging questions in the diagnosis of cancer subtypes. This is an important step in the effective treatment of cancer because it means that patients can potentially receive the correct treatment faster and without having unwanted side effects
14 Scientific Computing World Summer 2020
from drugs that may be ineffective. ‘The big question in clinical care is who is going to respond to which therapy. Once a diagnosis has been made, what subtype cancer is it that determines the kind of therapies that a patient is going to get.’
There are a number of existing molecular markers that can help to identify potential treatments but while ‘they are good, they are not good enough,’ argues Ruderman. ‘For example, if there is a mutation in a patient’s melanoma to the BRAF gene then they would be given Zelburaf as the drug to treat them. But despite having this molecular marker only around half of those patients are going to respond to that drug,’ added Ruderman. ‘What is common to all of these is that patients are going to have to suffer the side effects of these therapies but not achieve any benefit. The question that researchers at the Ellison Institute began to ask themselves
“Can we use AI and all the wonderful advances in computer vision to “read the tea leaves” and find things that are too subtle for our eyes to discern?”
is: ‘can AI help to make better decisions in these cases to predict, not just which patients have these markers but which patients will respond?’ The initial research looked at hormone
receptor positive breast cancer. Traditionally this is done with a $300 immuno-histochemistry test, if a sample is found to be estrogen receptor positive (ER+) then approximately 60 per cent of patients respond to endocrine therapy such as Tamoxifen. ‘Fundamentally these two cancers look the same under the microscope. If you ask a pathologist to designate just by eye from this kind of scan – is it an ER+ or ER- tumour? They generally would not be able to do it,’ stated Ruderman. ‘There are
certain subtypes such as lobular cancers where the cells all arrange in lines which could be identified as an ER+ cancer but that is a fairly rare subtype.’ ‘Can we use AI and all the wonderful
advances in computer vision to “read the tea leaves” and find things that are too subtle for our eyes to discern?’ The slides are digitised and then broken
down into different measurable variables such as size of nuclei, orientation and so on, and this is fed into a deep (more than five layers) neural network. Testing on non trained samples enabled
the researchers to create an ROC curve, thich is a graphical plot that illustrates the diagnostic ability of a binary classifier system. Initial area under the curve (AOC) was found to be 0.72. While initial results were promising
several iterations of the network and advances in organisation, compression and fingerprint technologies has meant that the accuracy of prediction using the H&E stain has risen dramatically. AUC in the later tests was found to be 0.89 which provides a much better standard for diagnosis.
Running on the Oracle Cloud
Infrastructure, researchers at the Ellison Institute bare metal GPU instances that are running and training these deep convolutional neural networks. They are also making use of Oracle Autonomous Database to store a lot of their training and inference data. ‘We have tens of millions of rows in these database tables,’ comments Ruderman. ‘Then, on the shared file system, we have the whole slide images. Terabytes of data from all of these slides that have data captured.’ The hope for the future is to take multiple slides and produce a 3D model which could provide much greater accuracy as the amount of data contained in each patient sample would increase dramatically. Ruderman also hopes that the work done here and at other research centres looking at different cancer types or other diagnosis can help to develop the frameworks for wider research into this combination of medicine, AI and computing vision.
@scwmagazine |
www.scientific-computing.com
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