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Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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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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Cancer02:18

Cancer

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Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
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Updated: Jul 13, 2025

Author Spotlight: Advancing Personalized Medicine in Ovarian Cancer
08:26

Author Spotlight: Advancing Personalized Medicine in Ovarian Cancer

Published on: February 23, 2024

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个性化乳腺癌查 个性化乳腺癌查

Dimitris Bertsimas1, Yu Ma1, Omid Nohadani2

  • 1Sloan School of Management and Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA.

JCO clinical cancer informatics
|October 16, 2023
PubMed
概括

使用患者数据进行个性化癌症查,显著减少了诊断延迟. 这种方法改善了与基于年龄的指导方针相比的早期检测,有利于患者的治疗结果.

科学领域:

  • 在瘤学瘤学.
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 目前的癌症查指南主要使用患者的年龄,可能导致延迟或过度查.
  • 现有的系统缺乏互操作性,阻碍了医疗保健提供者之间的患者数据集成.
  • 在标准化查方案中,个人医疗特征往往被忽视.

研究的目的:

  • 开发一种使用索赔数据的临床支持工具,以提高医生在癌症查中的决策能力.
  • 为个性化,动态和数据驱动的癌症查建议创建一个机器学习框架.
  • 通过结合个体患者数据来解决以年龄为中心的查的局限性.

主要方法:

  • 使用了索赔数据和医疗保险交易,用于诊断,程序和药物的标准化编码.
  • 开发了一个新的机器学习框架,以生成个性化的查建议.
  • 将该方法应用于乳腺癌乳房扫描查,使用378,840名女性患者的数据.

主要成果:

  • 个性化查表明,在不同风险人群中,平均癌症诊断延迟2至3个月的时间有统计学上的显著减少.
  • 个体患者的福利表现出更大的改善,延迟减少了10个月.
  • 该研究强调了数据驱动方法在优化查时间表方面的有效性.

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结论:

  • 将个人医疗特征和机器学习整合到癌症查中,可以提高及时性,并适应不断变化的患者风险.
  • 拟议的方法为临床医生提供了有价值的支持工具,改善查决策.
  • 未来在医疗保健机构的实施可能会导致更有效和个性化的癌症护理途径.