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相关概念视频

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

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,...
Classification of Illness01:17

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

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.
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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Statistical Software for Data Analysis and Clinical Trials01:12

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

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Updated: May 8, 2026

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
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使用大型语言模型开发用于甲状腺癌分期和风险级别分类的命名实体框架.

Matrix M H Fung1, Eric H M Tang2,3, Tingting Wu2

  • 1Division of Endocrine Surgery, Department of Surgery, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.

NPJ digital medicine
|March 2, 2025
PubMed
概括
此摘要是机器生成的。

我们使用大型语言模型 (LLM) 创建了一个框架,从甲状腺癌临床笔记中提取癌症阶段和风险信息. 这种方法高效准确地分类了差异很好的甲状腺癌患者.

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科学领域:

  • 在瘤学瘤学.
  • 医疗信息学 医疗信息学
  • 人工智能的人工智能

背景情况:

  • 对于患者管理来说,分类差异很好的甲状腺癌阶段和风险至关重要.
  • 从临床笔记中提取这些信息可能是具有挑战性的,因为半结构化数据.
  • 现有的方法在处理大数据集时可能缺乏效率和准确性.

研究的目的:

  • 开发一个命名实体 (NE) 框架,从癌症基因组图谱-甲状腺癌 (TCGA-THCA) 数据库中提取信息.
  • 评估大型语言模型 (LLM) 用于分类美国癌症联合委员会 (AJCC) 阶段和美国甲状腺协会 (ATA) 风险类别.
  • 为了优化分类甲状腺癌阶段和风险的效率和准确性.

主要方法:

  • 开发了一个NE框架,包括注释准则,基本真相标签,提示策略和评估代码.
  • 使用了四个LLM (Mistral-7B-Instruct,Llama-3.1-8B-Instruct,Gemma-2-9B-Instruct,Qwen2.5-7B-Instruct) 来进行离线信息提取.
  • 采用集体式多数投票策略进行分类,通过TCGA-THCA病理说明和伪临床病例进行验证.

主要成果:

  • 该NE框架是使用分别50和289个TCGA-THCA笔记开发和验证的.
  • 一个整体策略在分类AJCC分期和ATA风险类别方面取得了令人满意的表现.
  • 开发的框架和分类器证明了最佳的效率和准确性.

结论:

  • 拟议的NE框架和基于LLM的组合分类器有效地提取和分类甲状腺癌的关键临床信息.
  • 这种方法提高了确定AJCC分期和ATA风险类别的准确性和效率.
  • 这项研究为分析大规模甲状腺癌数据提供了宝贵的工具.