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More precision for personalized medicine
October 2016
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NEW
YORK—The Department of Pathology at the Icahn School of Medicine at Mount Sinai has
established the Center for Computational and Systems Pathology to explore efforts to more accurately classify diseases and guide treatment using computer
vision and machine learning techniques. Using advanced computer science and mathematical techniques with cutting-edge microscope technology and artificial
intelligence, the center hopes to “revolutionize pathology practice,” according to a press release.
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.
Dr. Carlos Cordon-Cardo, who will oversee the new center located at
Mount Sinai St. Luke’s, will continue to be chair of the department of pathology at the Mount Sinai Health System and a professor of pathology,
genetics and genomic sciences and oncological sciences at the Icahn School of Medicine. Dr. Gerardo Fernandez, an associate professor of pathology and
genetics and genomic sciences at the Icahn School of Medicine at Mount Sinai, will serve as the center’s medical director. He will work closely with
Dr. Michael Donovan, a research professor of pathology at the Icahn School of Medicine, and Dr. Jack Zeineh, 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,” said 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, reportedly making it the largest department of its kind in the country. “Computer vision and machine learning techniques are used by our
group to remove subjectivity out of the process of microscopically characterizing disease,” Fernandez explained. “Image analysis and
computational approaches open up the possibility of developing features that were previously too subtle or too complex to be robust and reproducible in an
analog environment.”
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,” said Fernandez. “Computer vision analysis, leveraging multispectral
fluorescence microscopic imaging, enables us to see what the human eye cannot. Characterizing complex biomarker and morphological relationships in patient
cohorts with known outcome allows us to identify patterns that correlate with behavior.”
He added,
“Intramural collaborations are encouraged, making the precise platform available to researchers interested in a computational pathology and systems
pathology approach to help answer question in their respective areas of research. The goal of each collaboration is dependent on the question being addressed
by each researcher. Goals may include better prognostic stratification of patients or more accurate therapeutic guidelines among many others.”
In its initial phase, 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. A second, higher-impact test, which will be used to characterize prostate cancer in newly diagnosed patients, will follow in 2017. At
that time, 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.
This works hopes to enable more precise cancer treatment “By using patient-specific phenotypic characteristics at
the critical decision point to establish the likelihood that a patient’s cancer will be appropriately treated,” Fernandez summarized.
Code: E101623 Back |
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