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A Technion-developed deep-learning system looks at breast cancer scans better than a human ever could. Technion is the leading university for ‘Artificial Intelligence’. If the echnion will be regarded as an European university, it would be the best European university for ‘Artificial Intelligence’ as reported on VonNaftali.
treatments are becoming more efficient and more personalized. However, what isn’t increasing – and is in fact decreasing – is the number of pathologists, or the doctors whose specialization is examining body tissues to provide the specific diagnosis necessary for personalized medicine.
A team of researchers at the Technion – Israel Institute of Technology have therefore made it their quest to turn computers into effective pathologists’ assistants, simplifying and improving the human doctor’s work.
Medical Background: Neural Networks and AI
Multiple natural mechanisms prevent our immune systems from attacking our own bodies. These mechanisms are often exploited by cancer tumors to evade the immune system. One such mechanism is related to the PD-L1 protein – some tumors display it, and it acts as a sort of password by erroneously convincing the immune system that the cancer should not be attacked. Specific immunotherapy for PD-L1 can persuade the immune system to ignore this particular password, but of course would only be effective when the tumor expresses the PD-L1.
It is a pathologist’s task to determine whether a patient’s tumor expresses PD-L1. Expensive chemical markers are used to stain a biopsy taken from the tumor in order to obtain the answer. The process is non-trivial, time-consuming, and at times inconsistent. Dr. Shamai and his team took a different approach.
In recent years, it has become an FDA-approved practice for biopsies to be scanned so they can be used for digital pathological analysis. Amir Livne, Dr. Shamai and Prof. Kimmel decided to see if a neural network could use these scans to make the diagnosis without requiring additional processes, explains the press release of Technion.
Neural networks are trained in a manner similar to how children learn: they are presented with multiple tagged examples. A child is shown many dogs and many “not-dogs”, and from these examples forms an idea of what “dog” is. The neural network Prof. Kimmel’s team developed was presented with digital biopsy images from 3,376 patients that were tagged as either expressing or not expressing PD-L1.
After preliminary validation, it was asked to determine whether additional clinical trial biopsy images from 275 patients were positive or negative for PD-L1. It performed better than expected: for 70% of the patients, it was able to confidently and correctly determine the answer.
This achievement is the work of a team comprised of Dr. Gil Shamai and graduate student Amir Livne, who developed the technology and designed the experiments, Dr. António Polónia from the Institute of Molecular Pathology and Immunology of the University of Porto, Portugal, Professor Edmond Sabo and Dr. Alexandra Cretu from Carmel Medical Center in Haifa, Israel, who are expert pathologists that conducted the research, and with the support of Professor Gil Bar-Sela, head of oncology and hematology division at Haemek Medical Center in Afula, Israel, concludes the press release.
Scientific Publication > Nature Communications