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相关概念视频

Positron Emission Tomography01:29

Positron Emission Tomography

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
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错误缓解使量子计算机上的PET放射性癌症特征成为可能.

S Moradi1, Clemens Spielvogel2, Denis Krajnc3

  • 1Applied Quantum Computing Group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, T1090, Vienna, Austria.

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|August 4, 2023
PubMed
概括

量子机器学习在预测PET扫描的癌症结果方面表现有前途,在真实量子硬件上的经典方法中表现优于误差缓解. 这一进步为癌症诊断和患者分层提供了潜在的飞跃.

关键词:
癌症 癌症 癌症 癌症机器学习是机器学习.在这里,PET是PET.量子计算是一种量子计算.无线电学 (Radiomics) 是一种放射学.

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科学领域:

  • 量子计算在医学中的应用.
  • 放射学和医学成像分析分析.
  • 机器学习用于临床终点预测.

背景情况:

  • 癌症诊断依赖于活检,这在特征瘤异质性方面存在局限性.
  • 以正子发射断层扫描 (PET) 驱动的放射学显示出预测临床结果的潜力.
  • 量子机器学习 (QML) 正在探索在癌症患者中增强预测能力.

研究的目的:

  • 评估量子机器学习 (QML) 在使用PET放射学数据预测临床终点方面的附加值.
  • 在模拟和真实量子计算机上比较QML性能与经典机器学习 (CML).
  • 调查误差缓解技术对QML预测准确性的影响.

主要方法:

  • 利用公开可用的PET放射学数据集用于质瘤,前列腺癌和肺癌.
  • 应用冗余减少和特征选择,创建了18个数据集变体.
  • 在模拟器中训练并测试了五个CML及其QML对应物,并在IonQ Aria量子计算机上选择了QML模型.

主要成果:

  • 在模拟器环境中,QML通常表现优于CML,特别是具有16个特征 (70%与69%BACC).
  • 错误缓解显著改善了IonQ设备上的QML性能,将测试BACC从69.94%提高到75.66%.
  • 在模拟器环境和真实量子硬件中观察到量子优势,当使用具有误差缓解的QML时.

结论:

  • 量子优势可以在真实量子计算机中实现,用于预测PET癌症队列中的临床终点,特别是在错误缓解方面.
  • 使用放射性数据,QML证明了在癌症患者分层中提高预测准确性的潜力.
  • 这项研究强调了将量子计算应用于医学诊断和预后的可行性和好处.