文章目录

使用多模态真实世界数据和可解释的人工智能解码泛癌症治疗结果

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence 原文发布日期：2025-01-30

英文摘要：

摘要翻译：

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文章：

使用多模态真实世界数据和可解释的人工智能解码泛癌症治疗结果

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence 原文发布日期：2025-01-30

英文摘要：

Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-derived (AID) markers for clinical decision support. We used xAI to decode the outcome of 15,726 patients across 38 solid cancer entities based on 350 markers, including clinical records, image-derived body compositions, and mutational tumor profiles. xAI determined the prognostic contribution of each clinical marker at the patient level and identified 114 key markers that accounted for 90% of the neural network’s decision process. Moreover, xAI enabled us to uncover 1,373 prognostic interactions between markers. Our approach was validated in an independent cohort of 3,288 patients with lung cancer from a US nationwide electronic health record-derived database. These results show the potential of xAI to transform the assessment of clinical variables and enable personalized, data-driven cancer care.

摘要翻译：

尽管精准放疗取得了进展，但临床决策仍然依赖于有限的变量和专家知识。为了克服这一限制，我们将多模态实时数据和可解释的人工智能（xAI）结合在一起，引入了AI导出（AID）标记以支持临床决策。我们利用xAI分析了基于38种实体瘤的15,726名患者的1,373种标记的结果，包括临床记录、体组成和突变肿瘤剖面图。xAI确定了每个临床标记对患者预后贡献的比例，并识别出114个关键标记，这些标记占据了神经网络决策过程的90%。此外，xAI还帮助我们发现了1,373种标记间的预后相互作用。我们的方法在来自美国全国电子健康记录数据库的独立队列中的肺癌患者中进行了验证（共3,288名患者）。这些结果表明，xAI有潜力改变临床变量评估并实现个性化、数据驱动的癌症护理。