儿科神经瘤学中的放射病态方法:机遇和挑战
Ariana M Familiar1, Aria Mahtabfar1,2, Anahita Fathi Kazerooni1,3,4
1Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Neuro-oncology advances
|October 16, 2023
概括
将人工智能 (AI) 与放射性和病态数据相结合,可以推进个性化癌症医学. 数据管理和人工智能方法的系统性变化对于儿科脑瘤研究和改善患者结果至关重要.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 云计算使医疗软件的可扩展存储和计算成为可能.
- 预测性AI模型越来越多地用于临床决策和个性化癌症医学.
- 神经瘤学从分析放射学和组织学图像中获益.
研究的目的:
- 在临床应用中审查病理学和综合放射学,病理学和基因组学研究.
- 讨论儿科脑瘤转化研究中的挑战.
- 概述多模式数据集成的技术和分析解决方案.
主要方法:
- 审查最近关于病态和综合数据分析的研究.
- 讨论数据聚合和建模瘤异质性的挑战.
- 建议对数据管理和人工智能实施进行系统变革.
主要成果:
- 综合的放射性和病态数据显示了精准医学的协同潜力.
- 多模式数据利用在数据聚合和建模异质性方面面临挑战.
- 以人工智能为动力的方法对于推进预测模型至关重要.
结论:
- 无线电病理学需要跨学科数据管理的系统性变化.
- 多模式数据集需要端到端的软件平台.
- 人工智能的进步可以改善预测模型的性能,以获得更好的癌症治疗方法.
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