Screen Them All: High-Throughput Pan-Cancer Genetic and Phenotypic Biomarker Screening from H&E Whole Slide Images
Aug 18, 2024·,,,,,,,
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Yi Kan Wang
Ludmila Tydlitatova
Jeremy D. Kunz
Gerard Oakley
Bonnie Kar Bo Chow
Ran A. Godrich
Matthew C. H. Lee
Hamed Aghdam
Alican Bozkurt
Michal Zelechowski
Chad Vanderbilt
Christopher Kanan
Juan A. Retamero
Peter Hamilton
Razik Yousfi
Thomas J. Fuchs
David S. Klimstra
Siqi Liu
Abstract
Molecular assays are standard of care for detecting genomic alterations in cancer prognosis and therapy selection but are costly, tissue-destructive and time-consuming. Artificial intelligence (AI) applied to routine hematoxylin and eosin (H&E)-stained whole slide images (WSIs) offers a fast and economical alternative for screening molecular biomarkers. We introduce OmniScreen, a high-throughput AI-based system leveraging Virchow2 embeddings extracted from 60,529 cancer patients with paired 489-gene MSK-IMPACT targeted biomarker panel and WSIs. Unlike conventional approaches that train separate models for each biomarker, OmniScreen employs a unified model to predict a broad range of clinically relevant biomarkers across cancers, including low-prevalence targets impractical to model individually. OmniScreen reliably identifies therapeutic targets and shared phenotypic features across common and rare tumors.
Type
Publication
arXiv preprint arXiv:2408.09554 (2024)
Authors
AI Scientist
I am an AI Scientist at Paige AI. I did my Ph.D. with Jennifer Dy, Dana Brooks, and Jan-Willem van de Meent at Northeastern University. My main research interests are machine learning with emphasis on probabilistic programming, deep neural networks, and their applications in biomedical image processing. I am one of the developers of Probabilistic Torch, a library for deep generative models that extends PyTorch. I am also one of the maintainers of the PyTorch distributions module.