SEP 27, 2016 02:22 PM PDT

Machine Outperforms Doctors at Diagnosing Brain Cancer

“Come in, Mr. Smith. The computer is ready for your diagnosis now.” This could conceivably be in store for brain cancer patients in the future, as a new computer program shows nearly twice the accuracy of doctors at diagnosing the tumors.
Computer training and machine learning has come a long ways – machines have beat humans in Jeopardy!, chess, even Go. In August 2016, machine learning even helped to rekindle seemingly deadened nerves, giving paraplegics limb sensations they hadn’t felt in years. Now, Case Western Reserve researchers say their computer program can tell whether abnormal MRI scans are indicative of dead tissues (radiation necrosis) or the recurrence of brain cancer. And, impressively, the program can do this better than trained neuroradiologists.

MRI scans of patients with radiation necrosis (above) and cancer recurrence (below) are shown in the left column. | Image: Tiwari Lab
"One of the biggest challenges with the evaluation of brain tumor treatment is distinguishing between the confounding effects of radiation and cancer recurrence," said Pallavi Tiwari, assistant professor of biomedical engineering at Case Western Reserve and leader of the study. "On an MRI, they look very similar."
To better distinguish between radiation necrosis and cancer recurrence, the team trained a computer to read radiomic features from an image scan. These features include textures and densities that can’t be gleaned with the naked eye.
"What the algorithms see that the radiologists don't are the subtle differences in quantitative measurements of tumor heterogeneity and breakdown in microarchitecture on MRI, which are higher for tumor recurrence," said Tiwari.

The algorithm also takes into account the edge space of a mass. This can reveal important information about the identity of the mass. "If the edges all point to the same direction, the architecture is preserved," said Anant Madabhushi, professor at Case Western who is co-author on the study. "If they point in different directions, the architecture is disrupted -- the entropy, or disorder, and heterogeneity are higher."
In a head-to-head test, the team gave 15 MRI scans to 2 physicians and compared the results to that from the computer algorithm. The algorithm was able to correctly diagnose more cases (12 total) than either of the physicians (7 and 8 cases each).
While it may still be unreasonable to solely rely on a computer algorithm to read brain cancer scans, the team hopes this tool will augment the accuracy of diagnoses for patients. Importantly, the right diagnosis can eliminate unnecessary biopsies and other procedures that are both extremely stressful and costly. 

Additional source: Case Western Reserve University
About the Author
  • I am a human geneticist, passionate about telling stories to make science more engaging and approachable. Find more of my writing at the Hopkins BioMedical Odyssey blog and at
You May Also Like
MAY 30, 2018
Cell & Molecular Biology
MAY 30, 2018
Diagnosing Deadly Kidney Cancers Sooner
Researchers have found that deadly kidney cancers can be identified by assessing their evolutionary path, which is different for distinct types....
JUN 16, 2018
JUN 16, 2018
What is Preimplantation Genetic Diagnosis?
All parents want a healthy baby, and for some people that carry genetic mutations, the decision to have children presents a challenge....
SEP 21, 2018
Clinical & Molecular DX
SEP 21, 2018
We will teach you to be a doctor, said us to the machine.
Clinical diagnosis are now possible in the hands of computers through machine learning. Whence, the reports of successful trials for the diagnostics....
OCT 29, 2018
Cannabis Sciences
OCT 29, 2018
Can Marijuana be Toxic?
Many users of marijuana believe that it is relatively safe, particularly because, as believed, one cannot overdose on the drug. ...
OCT 29, 2018
OCT 29, 2018
Immunity for All
A study published in Nature has shed light on the evolution of immune system genes across species with great detail at the single-cell level...
DEC 04, 2018
DEC 04, 2018
Natural HIV Resistance
A team of scientists discover unique immune reactions to the one percent of HIV infected patients that are able to fend off viral propagation without antiretroviral therapy...
Loading Comments...