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相关概念视频

Targeted Cancer Therapies02:57

Targeted Cancer Therapies

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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
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Treatment Resistant Cancers02:56

Treatment Resistant Cancers

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Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
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Combination Therapies and Personalized Medicine02:50

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Clinical Trials: Overview01:11

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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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相关实验视频

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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从非小细胞肺癌临床笔记中提取治疗和反应,使用自然语言处理.

Sonish Sivarajkumar1, Subhash Edupuganti2, David Lazris2,3

  • 1Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA.

JCO clinical cancer informatics
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概括

这项研究开发了一个自然语言处理 (NLP) 系统,自动从临床笔记中提取癌症治疗和患者反应,改善非小细胞肺癌 (NSCLC) 的现实世界证据 (RWE) 生成. 该系统在将治疗方法与结果联系起来方面取得了很高的准确性.

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科学领域:

  • 计算瘤学是一种计算瘤学.
  • 生物医学信息学是生物医学信息学.
  • 自然语言处理 (NLP) 是一种自然语言处理.

背景情况:

  • 从临床笔记中手动提取癌症治疗结果是耗时的,对现实世界的证据 (RWE) 产生具有挑战性.
  • 自动化这一过程对于有效分析瘤学数据至关重要.

研究的目的:

  • 开发和验证一个强大的NLP系统,自动从非小细胞肺癌 (NSCLC) 临床笔记中提取癌症治疗和基于RECIST的反应类别.
  • 评估系统在将治疗与患者反应联系起来方面的表现.

主要方法:

  • 一项回顾性研究使用250条注释的NSCLC瘤学笔记.
  • 一个端到端的NLP管道,结合基于规则的实体提取和用于关系分类的机器学习模型 (生物医学临床BERT).
  • 在持有测试集上的性能评估和部分外部验证.

主要成果:

  • 该NLP系统的准确性很高,F1分数为0.92的关系分类和宏观平均F1分数为0.87的实体提取在UPMC测试集.
  • 对于化疗和大多数反应类型,观察到高精度.
  • 外部验证显示中等关系提取性能 (F1:0.51-0.64).

结论:

  • 开发的NLP系统可靠地从非结构化的NSCLC瘤记录中提取结构化的治疗和反应信息,并具有高准确性.
  • 这种自动化方法促进了关键癌症治疗结果的抽象,简化了瘤学中的RWE生成.