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Targeted Cancer Therapies02:57

Targeted Cancer Therapies

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 specific...

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基于知识的与基于深度学习的治疗计划为乳腺放射治疗.

Daniel Portik1, Enrico Clementel1, Jérôme Krayenbühl2

  • 1European Organisation for Research and Treatment of Cancer (EORTC) Headquarters, Brussels, Belgium.

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概括

基于知识的规划 (KBP) 和深度学习 (DL) 模型显示乳腺癌放射治疗规划的临床可接受结果. 当使用强大的数据集进行这些先进的规划技术时,不需要清理数据.

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科学领域:

  • 医学物理 医学物理
  • 辐射瘤学 辐射瘤学
  • 人工智能在医学中的应用

背景情况:

  • 先进的放射治疗 (RT) 计划旨在提高效率和质量.
  • 基于知识的规划 (KBP) 和深度学习 (DL) 是新兴的解决方案.
  • 需要直接比较KBP和DL模型用于乳腺癌规划.

研究的目的:

  • 直接比较乳腺癌RT规划的KBP和DL模型.
  • 使用相同的培训,验证和测试数据集来评估计划质量.
  • 评估数据清理对KBP模型性能的影响.

主要方法:

  • 训练了两个KBP模型 (干净和非干净的数据集) 和一个DL U-net模型.
  • 用了90个左侧乳腺癌的RT计划 (15个分数,每个为2.6 Gy) 进行培训/验证.
  • 评估了15个独立的患者计划,使用剂量-体积组图参数与临床计划对比.

主要成果:

  • 与临床计划相比,KBP模型和DL U-net模型都显示了剂量计算的小差异.
  • KBP模型最初低估了心脏和肺部的平均剂量,但最终的肺部平均剂量更高.
  • DL U-net 模型实现了与临床计划相比的平均规划目标体量剂量.

结论:

  • KBP和DL模型为乳腺癌放射治疗提供了临床上可接受的结果.
  • 当使用高质量的数据集时,KBP模型不需要清理数据.
  • 无论是KBP还是DL,都为乳腺癌的手动规划提供了可行的替代方案.