数据中心的人工智能和癌症研究:构建一个真实世界的头治疗数据库
V Butterworth1,2, T Young1,2, H Drake1,2
1Guy's and St Thomas' NHS Foundation Trust (GSTT), London, UK.
概括
我们为头癌 (HNC) 成像和放射治疗数据开发了一个联合数据湖,简化了机器学习 (ML) 模型的生产. 这种方法大大减少了数据准备时间,提高了研究效率.
科学领域:
- 医学成像分析 医学成像分析
- 机器学习在瘤学中的应用
- 放射治疗研究 放射治疗研究
背景情况:
- 高质量的数据对于机器学习 (ML) 模型的性能和通用性至关重要.
- 研究的临床数据收集在数据保护和患者隐私方面面临挑战.
- 手动数据提取和注释耗时,延迟了ML模型的开发.
研究的目的:
- 提出一个新的机构联合数据湖头癌 (HNC) 成像和放射治疗 (RT) 数据.
- 为了促进ML模型的高效生产和现实世界的证据生成.
- 描述符合治理的流程,并为类似的数据库设置提供框架.
主要方法:
- 使用开源软件创建结构化数据管道.
- 实现了可扩展的神经成像存档工具包 (XNAT) 作为一个安全的成像平台.
- 在人工智能项目中为联合学习建立了本地安全的飞地.
主要成果:
- 创建了一个临床注释数据湖,有2895名同意HNC患者.
- 该数据集包括22,170个成像集,涵盖诊断,分期,治疗和监测.
- 复制的关键建议涉及基础设施,选择标准,同意和隐私.
结论:
- 开发的数据库是HNC RT研究的安全和可扩展的工具.
- 它彻底改变了机器学习模型生产的时间和成本.
- 这个设置使得这个过程更安全,更快,更高效.
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