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English(EN) Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy

新的 MSB 框架提高了肿瘤学中的生存预测能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个名为“具有块状缺失值的多模态堆叠”（MSB）的新框架，以应对临床肿瘤学中整合多模态数据集的挑战。MSB 是一个专为生存分析设计的晚期融合框架，可以处理某些患者子集中整个数据源不可用的情况。当应用于 PIONeeR 研究以预测肺癌患者的无进展生存期时，MSB 证明其预测性能优于现有算法，显著缩小了泛化差距，并识别了关键的预测生物标志物。 AI

排序理由 该集群包含一篇学术论文，详细介绍了肿瘤学中生存分析的新统计框架。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mohamed Boussena, Florence Monville, Jacques Fieschi-Meric, Frederic Vely, Pierre Milpied, Julien Mazieres, Maurice Perol, Eric Vivier, Laurent Greillier, Fabrice Barlesi, Sebastien Benzekry ·

    Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy

    arXiv:2605.25050v1 Announce Type: cross Abstract: Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets. Standard survival models often s…

  2. arXiv stat.ML TIER_1 English(EN) · Sebastien Benzekry ·

    Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy

    Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets. Standard survival models often struggle with these gaps, leading to biased results…

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