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

NHS Staff Punished as 500,000 Rely on Wh…

WhatsApp, Facebook Messenger and other unauthorised instant messaging (IM) apps are being used by approximately 500,000 NHS staff at work, as a growing number turn to consumer tools to communicate...

Call for Abstracts: European Telemedicin…

27 - 29 May 2018, Sitges, Barcelona, Spain. The European Telemedicine Conference 2018 (ETC18) is an interdisciplinary forum for healthcare professionals, directors, managers, and researchers with the intent of bringing together...

conhIT 2018: The stage is Set for Dialog…

17 - 19 April 2018, Berlin, Germany. Finding out about and supporting all aspects of the digital transformation of the healthcare system: that is what this year's conhIT, Europe's largest event...

Smartphone 'Scores' can Help Doctors Tra…

Parkinson's disease, a progressive brain disorder, is often tough to treat effectively because symptoms, such as tremors and walking difficulties, can vary dramatically over a period of days, or even...

Focus on the Digital Transformation - A …

17 - 19 April 2018, Berlin, Germany. How is the digitalisation of the healthcare system affecting the relationship between patients and doctors? What new innovations and solutions does the health IT...

Portable Device Detects Severe Stroke in…

A new device worn like a visor can detect emergent large-vessel occlusion in patients with suspected stroke with 92 percent accuracy, report clinical investigators at the Medical University of South...

Imitation is the Most Sincere Form of Fl…

For every two mobile apps released, one is a clone of an existing app. However, new research published in the INFORMS journal Information Systems Research shows the success of the...

Merck Partners with Medisafe to Help Imp…

Merck, a leading science and technology company, today announced a new collaboration with US-based Medisafe to help its cardiometabolic patients better manage medication intake and adhere to prescribed treatment regimens...

Philips Research-led Big Data Consortium…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, together with its consortium partners, today announced that it has received funding from the EU's Horizon 2020 program...

Smartphone App Performs Better than Trad…

A smartphone application using the phone's camera function performed better than traditional physical examination to assess blood flow in a wrist artery for patients undergoing coronary angiography, according to a...

Deep Learning Transforms Smartphone Micr…

Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones...