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English(EN) A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology

新的多模态基础模型增强了肿瘤学患者表征

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为oFM的多模态基础模型，旨在表征肿瘤学中患者随时间推移的状态。该模型整合了来自超过167万癌症患者的临床数据以及DNA、RNA和H&E病理信息。oFM在预测治疗反应、无进展生存期和总生存期方面表现出显著的改进，在预后基准和比较治疗队列中优于基线特征。此外，一个基于oFM嵌入的框架可以将预测结果与临床相关机制联系起来，有助于临床和药物开发应用。 AI

影响 通过提供对癌症演变和治疗反应的更深入的见解，该模型有望显著改善患者的治疗效果并加速肿瘤学领域的药物开发。

排序理由 该集群包含一篇详细介绍肿瘤学新多模态基础模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的多模态基础模型增强了肿瘤学患者表征

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该集群包含一篇详细介绍肿瘤学新多模态基础模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [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 ·

    用于肿瘤学中纵向患者表征和可扩展洞察生成的模态基础模型

    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…

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