利用真实世界数据和人工智能改变乳腺癌管理

P. Heudel , B. Mery , H. Crochet , T. Bachelot , O. Tredan
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引用次数: 0

摘要

背景真实世界数据(RWD)为了解乳腺癌治疗的有效性和安全性提供了重要依据,尤其是在传统临床试验可能存在局限性的不同患者群体中。将真实世界数据整合到乳腺癌研究中能加深对治疗结果的理解,支持临床决策,补充对照临床研究的结果。设计本文回顾了将真实世界数据整合到乳腺癌研究中的情况,强调了其中的益处和挑战。文章研究了包括电子健康记录 (EHR)、保险理赔和患者登记在内的各种 RWD 来源,重点探讨了它们在三阴性乳腺癌研究中的应用。文章还探讨了人工智能(AI)在管理 RWD 中的作用,特别是通过自然语言处理(NLP)和预测分析等技术来加强数据收集、存储和分析。将人工智能整合到 RWD 管理中,可以更深入地了解患者的治疗效果,支持个性化治疗策略。利用 RWD 进行的具体研究表明,人们对三阴性乳腺癌等乳腺癌亚型的了解有所加深,治疗方案的有效性也有所提高。应对这些挑战需要强有力的数据管理框架、跨学科合作以及对先进分析工具的投资。RWD 和人工智能在改变乳腺癌治疗和改善患者护理方面的潜力巨大,这也凸显了持续研究与合作的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transforming breast cancer management with real-world data and artificial intelligence

Background

Real-world data (RWD) provide essential insights into the effectiveness and safety of breast cancer treatments, particularly in diverse patient populations, where traditional clinical trials may have limitations. Integrating RWD into breast cancer research enhances the understanding of treatment outcomes and supports clinical decision-making, complementing the findings from controlled clinical studies.

Design

This article reviews the integration of RWD into breast cancer research, highlighting the benefits and challenges. Various sources of RWD, including electronic health records (EHRs), insurance claims, and patient registries, are examined, with a focus on their application in studies of triple-negative breast cancer. The article also explores the role of artificial intelligence (AI) in managing RWD, particularly through technologies like natural language processing (NLP) and predictive analytics, which enhance data collection, storage, and analysis.

Results

RWD has demonstrated significant value in informing clinical decision-making and improving patient outcomes in breast cancer treatment. The integration of AI into the management of RWD has provided deeper insights into patient outcomes and supported personalized treatment strategies. Specific studies leveraging RWD have shown improved understanding of breast cancer subtypes, such as triple-negative breast cancer, and enhanced the effectiveness of treatment protocols.

Conclusion

Despite the benefits, challenges remain in integrating RWD and AI into clinical practice, particularly regarding transparency, interpretability, and ethical considerations. Addressing these challenges requires robust data governance frameworks, interdisciplinary collaboration, and investment in advanced analytical tools. The potential for RWD and AI to transform breast cancer treatment and improve patient care is significant, underscoring the need for ongoing research and collaboration.

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