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New multimodal foundation model enhances oncology patient representation

Researchers have developed a multimodal foundation model called oFM, designed to represent patient states over time in oncology. This model integrates clinical data with DNA, RNA, and H&E pathology information from over 1.67 million cancer patients. The oFM demonstrated significant improvements in predicting treatment response, progression-free survival, and overall survival, outperforming baseline features in prognostic benchmarks and comparative-treatment cohorts. Additionally, a framework built on oFM embeddings can link predicted outcomes to clinically relevant mechanisms, aiding in clinical and drug-development applications. AI

IMPACT This model could significantly improve patient outcomes and accelerate drug development in oncology by providing deeper insights into cancer evolution and treatment response.

RANK_REASON The cluster contains a research paper detailing a new multimodal foundation model for oncology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New multimodal foundation model enhances oncology patient representation

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The cluster contains a research paper detailing a new multimodal foundation model for oncology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Eugene Vorontsov, Yi Kan Wang, Alican Bozkurt, Adam Casson, Ludmila Tydlitatova, Michal Zelechowski, Ezra E. W. Cohen, Jyoti D. Patel, Max Banaszak, Caitlin McWilliams, Shane Colley, Kate Sasser, Ryan Fukushima, Eric Lefkofsky, Razik Yousfi, Siqi Liu ·

    A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology

    arXiv:2608.24688v1 Announce Type: new Abstract: Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal observations. We introduce the oFM, a foundation model developed on a real-world on…