推进肿瘤学中的联邦学习：超越乳腺癌、肺癌和前列腺癌的边界——一项系统综述
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review
AI总结：
本系统综述评估联邦学习在乳腺癌、肺癌和前列腺癌中的应用，发现其在25项研究中15项优于集中式ML，能增强泛化性、性能和隐私，但面临可重复性等挑战，未来需进一步研究。
摘要：
联邦学习（FL）已成为解决肿瘤学中集中式机器学习（ML）局限性的有前景的方案，特别是在克服隐私问题以及利用多样化、多中心数据的力量方面。本系统综述综合了当前关于肿瘤学中前沿FL的知识，重点关注乳腺癌、肺癌和前列腺癌。与以往的综述不同，我们的全面综述批判性地评估了FL在癌症护理中的实际实施和影响，展示了其在临床环境和数据中增强ML泛化性、性能和数据隐私的有效性。我们评估了FL领域的最新进展，展示了其在数据隐私法规日益收紧的情况下被日益广泛采用。在审查的25项研究中，FL在15项中优于集中式ML，涵盖了多样化的ML模型和临床应用，并促进了多模态信息整合以用于精准医疗。尽管目前在可重复性、标准化和方法学方面存在挑战，但FL在利用真实世界数据和满足临床需求方面展现出的可证明益处，凸显了其在推进癌症研究方面的巨大潜力。我们建议未来的研究应侧重于解决这些局限性并探索更先进的FL方法，以充分利用数据多样性，实现尖端FL在癌症护理中的变革性力量。
英文摘要：
Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systematic review synthesises current knowledge on the state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Distinct from previous surveys, our comprehensive review critically evaluates the real-world implementation and impact of FL on cancer care, demonstrating its effectiveness in enhancing ML generalisability, performance and data privacy in clinical settings and data. We evaluated state-of-the-art advances in FL, demonstrating its growing adoption amid tightening data privacy regulations. FL outperformed centralised ML in 15 out of the 25 studies reviewed, spanning diverse ML models and clinical applications, and facilitating integration of multi-modal information for precision medicine. Despite the current challenges identified in reproducibility, standardisation and methodology across studies, the demonstrable benefits of FL in harnessing real-world data and addressing clinical needs highlight its significant potential for advancing cancer research. We propose that future research should focus on addressing these limitations and investigating further advanced FL methods, to fully harness data diversity and realise the transformative power of cutting-edge FL in cancer care.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
