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Advances in genomics have created many new cancer therapies that target specific genetic or molecular features, raising the potential for personalized treatment that improves effectiveness and decreases side effects. However, the majority of patients treated with targeted therapies do not respond as predicted, and detailed patient genomic information is expensive to acquire.

This collaboration proposes to improve targeting of cancer therapies by developing new AI approaches that recommend the best treatment based on a combination of genetic and pathology data. Researchers will build new methods drawing upon computer vision and machine learning to gather essential contextual information about individual cancers from tumor samples, utilizing both genomic and image-based features. This work will advance both artificial intelligence and cancer-focused data science by developing innovative solutions to emerging machine learning problems in cancer research, ultimately benefiting patients through more targeted, effective treatments.

[Image: “Adenoid cystic carcinoma” by Yale Rosen.]


Samantha Riesenfeld

Assistant Professor, Molecular Engineering and of Genetic Medicine

Alexander Pearson

Assistant Professor of Medicine, UChicago Medicine

Greg Shakhnarovich

Professor, Toyota Technological Institute at Chicago

Michael Maire

Assistant Professor, Department of Computer Science, UChicago

Thomas Brettin

Strategic Program Manager, Argonne National Laboratory

Yitan Zhu

Computational Scientist, Argonne National Laboratory

Robert Grossman

Frederick H. Rawson Distinguished Service Professor in Medicine and Computer Science; the Jim and Karen Frank Director, Center for Translational Data Science (CTDS)

Nicole Cipriani

Associate Professor of Pathology, UChicago Medicine