Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine

Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine

Abstract Background Precision oncology depends on identifying the biological vulnerabilities of a tumor. Molecular assays, like transcriptomics, provide an information-rich view of the tumor that can be leveraged to inform therapeutic selection. However, the costs of such assays can be prohibitive f...

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Main Authors: Hanna M. Hieromnimon, James Dolezal, Kristina Doytcheva, Frederick M. Howard, Sara Kochanny, Zhenyu Zhang, Robert L. Grossman, Kevin Tanager, Cindy Wang, Jakob Nikolas Kather, Evgeny Izumchenko, Nicole A. Cipriani, Elana J. Fertig, Alexander T. Pearson, Samantha J. Riesenfeld
Format: Article
Language:English
Published: BMC 2025-08-01
Series:Genome Medicine
Subjects:
Computational pathology
Multimodal learning
Gene expression
Synthetic histology
Deep learning
Precision oncology
Online Access:https://doi.org/10.1186/s13073-025-01502-z
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https://doi.org/10.1186/s13073-025-01502-z

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