Mount Sinai Establishes Center for Computational and Systems Pathology
News Aug 25, 2016
The Department of Pathology at the Icahn School of Medicine at Mount Sinai has established the Center for Computational and Systems Pathology to revolutionize pathology practice, using advanced computer science and mathematical techniques coupled with cutting-edge microscope technology and artificial intelligence. The goal of this new academic research facility is to explore efforts to more accurately classify diseases and guide treatment using computer vision and machine learning techniques.
The Center for Computational and Systems Pathology will be a hub for the development of new diagnostic, predictive, and prognostic tests and will partner with Mount Sinai-based “Precise Medical Diagnostics” (Precise MD), which has been under development for more than three years by a team of physicians, scientists, mathematicians, engineers, and programmers.
Carlos Cordon-Cardo, MD, PhD, will oversee the new center, located at Mount Sinai St. Luke’s, and will continue his role as Chair of the Department of Pathology at the Mount Sinai Health System and Professor of Pathology, Genetics and Genomic Sciences, and Oncological Sciences at the Icahn School of Medicine. Gerardo Fernandez, MD, Associate Professor of Pathology, and Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, will be the Center’s Medical Director. He will work closely with Michael Donovan, MD, PhD, Research Professor of Pathology at the Icahn School of Medicine, and Jack Zeineh, MD, Director of Technology for Precise MD.
“Our goal is to provide a precise mathematical approach to classifying and treating disease, which will assist our clinicians with information for effective patient care and health management,” says Dr. Cordon-Cardo. “By refining diagnoses, we can save patients from unnecessary treatments.”
Mount Sinai’s Department of Pathology processes more than 80 million tests a year, making it the largest department of its kind in the country.
Precise MD is developing new approaches to characterizing an individual’s cancer by combining multiple data sources and analyzing them with mathematical algorithms, offering a more sophisticated alternative to standard approaches. One such example is Precise MD’s approach to improve upon the Gleason score, a grading system that has been used since the 1960s to establish the prognosis for a prostate cancer and guide the patient’s treatment options.
“We’re characterizing tumors based on the combination of their architectural patterns and biomarkers,” says Dr. Fernandez. “Computer vision analysis, leveraging multispectral fluorescence microscopic imaging, enables us to see what the human eye cannot.”
In its initial phase this summer, Precise MD will complete a test used for patients who have had prostatectomies at Mount Sinai Health System, to help determine which of them are more likely to have a recurrence of cancer and may need additional therapy such as chemotherapy.
“Precise MD’s approach gives us an in-depth knowledge about biological behavior of prostate cancer and allows us to choose appropriate patients for active surveillance,” says Ash Tewari, MD, Chair of the Department of Urology at the Mount Sinai Health System and the Kyung Hyun Kim, MD Professor of Urology at the Icahn School of Medicine. “Due to their support, we have a large pool of patients who are with active surveillance.”
A second, higher-impact test will follow in 2017, which will be used to characterize prostate cancer in newly diagnosed patients. At that time, Dr. Cordon-Cardo says all prostate cancer patients at Mount Sinai will have the option to receive this test.
It is anticipated that in 2017 other current efforts will yield additional novel computer vision and machine learning tools to better characterize breast cancer. The Center for Computational and Systems Pathology and the Precise MD platform could eventually be used to characterize any number of disease states, including but not limited to melanoma, lung, and colon cancers as well as chronic inflammatory conditions such as inflammatory bowel disease.
Scientists want access to data collected by others for their research, but such access could also compromise personal privacy, even after removal of so-called personally identifiable data. Now, statisticians are developing synthetic networks that may increase the availability of some data while still protecting individual or institutional privacy.