Machines See the Future for Patients Diagnosed with Brain Tumors

For patients diagnosed with glioma, a deadly form of brain tumor, the future can be very uncertain. While gliomas are often fatal within two years of diagnosis, some patients can survive for 10 years or more. Predicting the course of a patient's disease at diagnosis is critical in selecting the right therapy and in helping patients and their families to plan their lives.

Researchers at Emory and Northwestern Universities recently developed artificial intelligence (AI) software that can predict the survival of patients diagnosed with glioma by examining data from tissue biopsies. The approach, described in Proceedings of the National Academy of Sciences, is more accurate than the predictions of doctors who undergo years of highly-specialized training for the same purpose.

Doctors currently use a combination of genomic tests and microscopic examination of tissues to predict how a patient's disease will behave clinically or respond to therapy. While genomic testing is reliable, these tests do not completely explain patient outcomes, and so microscopic examination is used to further refine prognosis. Microscopic examination, however, is very subjective, with different pathologists often providing different interpretations of the same case. These interpretations can impact critical decisions like whether a patient enrolls in an experimental clinical trial or receives radiation therapy as part of their treatment.

"Genomics have significantly improved how we diagnose and treat gliomas, but microscopic examination remains subjective. There are large opportunities for more systematic and clinically meaningful data extraction using computational approaches," says Daniel J. Brat, MD, PhD, the lead neuropathologist on the study, who began developing the software while at Emory University and the Winship Cancer Institute. Brat currently is chair of pathology at Northwestern University Feinberg School of Medicine.

The researchers used an approach called deep-learning to train the software to learn visual patterns associated with patient survival using microscopic images of brain tumor tissue samples. The breakthrough resulted from combining this advanced technology with more conventional methods that statisticians use to analyze patient outcomes. When the software was trained using both images and genomic data, its predictions of how long patients survive beyond diagnosis were more accurate than those of human pathologists. The study used public data produced by the National Cancer Institute's Cancer Genome Atlas project to develop and evaluate the algorithm.

"The eventual goal is to use this software to provide doctors with more accurate and consistent information. We want to identify patients where treatment can extend life," says Lee A.D. Cooper, PhD, the study's lead author, a professor of biomedical informatics at Emory University School of Medicine and member of the Winship Cancer Institute. "What the pathologists do with a microscope is amazing. That an algorithm can learn a complex skill like this was an unexpected result. This is more evidence that AI will have a profound impact in medicine, and we may experience this sooner than expected."

The researchers also demonstrated that the software learns to recognize many of the same structures and patterns in the tissues that pathologists use when performing their examinations. "Validation remains a barrier to using these algorithms in patient care. Being able to explain why an algorithm works is an important step towards clinical implementation."

The researchers are looking forward to future studies to evaluate whether the software can be used to improve outcomes for newly diagnosed patients.

Pooya Mobadersany, Safoora Yousefi, Mohamed Amgad, David A Gutman, Jill S Barnholtz-Sloan, José E Velázquez Vega, Daniel J Brat, Lee AD Cooper.
Predicting cancer outcomes from histology and genomics using convolutional networks.
PNAS March 12, 2018. 201717139. doi: 10.1073/pnas.1717139115.

Most Popular Now

Cerner Teams-Up with North London Partne…

Cerner has announced a new collaboration with North London Partners (NLP) - a partnership of health and care organisations across five London boroughs - to connect care information, share records...

Bayer Accelerates Six New Startups

Changing the experience of health: that's the focus of the six startups which the Bayer G4A team has included in the Accelerator program this year. The young companies from Canada...

Artificial Intelligence could Help Tackl…

Scientist from King’s College London believe that Artificial Intelligence could hold the key to identifying the best way to treat the country’s biggest killer, coronary heart disease (CHD). And now...

Researchers Use AI to Successfully Treat…

A translational research team led by the National University of Singapore (NUS) has harnessed CURATE.AI, a powerful artificial intelligence (AI) platform, to successfully treat a patient with advanced cancer, completely...

Greater Manchester Health and Social Car…

Greater Manchester Health and Social Care Partnership(GMHSC Partnership) aims to deliver rapid savings and identify how the cloud can support the region’s devolved vision for integratedhealth and careservices through work...

Philips Launches New Cardiac Ultrasound …

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, today introduced the EPIQ CVx cardiovascular ultrasound system. Built on the powerful EPIQ ultrasound platform, EPIQ CVx is...

Nottingham University Hospitals Switches…

Nottingham University Hospitals NHS Trust has completed a complex project to switch integration engines; on time, on budget, and with no disruption to services. The trust has a well-advanced 'best...

An Avatar Uses Your Gait to Predict How …

Humans instinctively adopt the gait that requires the least amount of energy given the walking conditions. Without realizing it, we are constantly tweaking our pace, stride length and foot lift...

Orion Health Secures Place on Framework …

Orion Health has secured a place on a framework contract that will give health and care organisations easier access to the technology they need to deliver the NHS reform agenda...

Study on Cross-Border Health Services: E…

The overall objective of this study was to propose recommendations for improving the current level of information provision to patients by National Contact Points (NCPs). The research methodology used in...

Social Media in the Pharmaceutical Indus…

21 - 23 January 2019, London, UK. SMi Group are delighted to present the return of their 11th annual Social Media in the Pharmaceutical Industry conference to London on the 21st...

Kids Connect with Robot Reading Partners

Kids learn better with a friend. They're more enthusiastic and understand more if they dig into a subject with a companion. But what if that companion is artificial? Researchers at...