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手工制作与手工制作. 深度放射学与融合与融合与融合. 深度学习:对基于机器学习的PET和SPECT成像中基于癌症结果预测的全面审查.

Mohammad R Salmanpour1,2,3, Somayeh Sadat Mehrnia4, Sajad Jabarzadeh Ghandilu5

  • 1Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, BC, Canada. msalman@bccrc.ca.

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概括

深度学习和放射学模型显示出使用PET/SPECT成像预测癌症结果的前景. 深度放射学特征 (DRF) 和融合模型表现出卓越的性能,尽管标准化和解释性挑战仍然存在.

关键词:
癌症 癌症 癌症 癌症深度学习 (Deep Learning) 是一种深度学习.深度放射学特征 深度放射学特征手工制作的放射学特征核医学是一种核医学.结果预测结果预测.

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科学领域:

  • 医学成像分析 医学成像分析
  • 机器学习在瘤学中
  • 无线电学和深度学习

背景情况:

  • 机器学习 (ML),包括深度学习 (DL) 和放射学,越来越多地用于PET和SPECT成像的癌症结果预测.
  • 不同ML技术的不一致性能 (手工制造的放射学特征 (HRF),深度放射学特征 (DRF),DL和混合融合模型) 需要进行比较分析.

研究的目的:

  • 系统地审查和比较使用PET/SPECT成像预测癌症结果的各种ML技术的性能.
  • 确定最有效的ML方法,并突出现有的局限性和现场挑战.

主要方法:

  • 对226项研究 (2020-2025) 的系统审查,将ML应用于PET/SPECT用于癌症预测结果.
  • 使用59项框架进行评估,包括数据集构建,特征提取,验证,可解释性和偏差.
  • 数据提取包括模型类型,癌症部位,成像方式,精度和AUC.

主要成果:

  • 基于PET的模型通常表现优于SPECT.
  • 深度放射学特征 (DRF) 模型实现了最高的平均精度 (0.862 ± 0.051).
  • 融合模型 (DRF,HRF,临床数据) 达到最高的AUC (0.861 ± 0.088),在准确度和AUC (p < 0.003) 中观察到显著差异.

结论:

  • 深度学习和基于DRF的模型,特别是当与HRF融合时,在PET/SPECT的癌症结果预测中优于仅使用HRF的方法.
  • 显著的局限性包括不良的阶级不平衡管理,缺失的数据,低的人口多样性,以及缺乏遵守像IBSI这样的标准化倡议.
  • 需要进一步的研究来解决可解释性和标准化问题,倡导统一的DRF提取框架.