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相关概念视频

Mouse Models of Cancer Study02:43

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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相关实验视频

Updated: Jan 16, 2026

Therapy Testing in a Spheroid-based 3D Cell Culture Model for Head and Neck Squamous Cell Carcinoma
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RT-HaND_C: 一个多源,验证的真实世界头癌数据集用于研究.

T Young1, H Drake2, V Butterworth1

  • 1Guy's and St Thomas' NHS Foundation Trust (GSTT), UK; King's College London, UK.

Clinical oncology (Royal College of Radiologists (Great Britain))
|October 4, 2025
PubMed
概括
此摘要是机器生成的。

一个新的头癌 (HNC) 数据集,RT-HaND_C,是使用现实世界数据 (RWD) 开发的. 这一全面的数据集揭示了HNC患者放射治疗后长期显著的体重减轻.

关键词:
人工智能的人工智能是人工智能.数据挖掘是一种数据挖掘.自然语言处理自然语言处理.现实世界的数据数据.

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科学领域:

  • 在瘤学瘤学.
  • 数据科学数据科学数据科学
  • 医疗信息学 医疗信息学

背景情况:

  • 现实世界数据 (RWD) 对头癌 (HNC) 患者的结果提供了宝贵的见解,特别是对于经常被排除在临床试验之外的多样化和并发症患者群体.
  • 在RWD质量的挑战需要严格的评估可靠的现实世界的证据生成.
  • 开发全面的HNC瘤学数据集对于推进研究至关重要.

研究的目的:

  • 开发一个大规模,高质量的HNC瘤学数据集,整合多个数据源.
  • 在HNC研究中建立一个强大的RWD评估框架.
  • 通过研究放射治疗后的长期体重趋势来评估开发的数据集的可用性.

主要方法:

  • 通过整合结构化和非结构化电子健康记录 (EHR) 数据,以及手动策划的信息,创建了RT-HaND_C数据集.
  • 使用经过验证的AI驱动自然语言处理工具来提取非结构化的EHR数据.
  • 整合了广泛的人口,疾病,治疗,结果和放射治疗剂量计数据从2010-2023,与严格的数据质量评估.

主要成果:

  • 该RT-HaND_C数据集包括2895名HNC患者,拥有超过190万个数据点和2000个数据类别.
  • 对大多数变量实现了>98%的准确性,在关键类别中数据的完整性和一致性很高.
  • 在放射治疗后的5年内,在HNC患者中显示出具有统计学意义的持续体重减轻,在6个月达到峰值.

结论:

  • RT-HaND_C是一个新的,高质量的真实世界数据资源,具有HNC研究的综合评估框架.
  • 该数据集通过与成像数据的虚拟链接来促进多模式研究.
  • RT-HaND_C可用于研究合作,目前正在努力提高其完整性并纳入前性数据.