磁共振成像和深度学习用于前列腺癌检测的整合:系统性审查
Deepak Kumar1,2,3, Priyank Yadav2, Kavindra Nath4
1Centre of Biomedical Research, Sanjay Gandhi Post Graduate Institute of Medical Sciences Campus Rae Bareli Road, Lucknow 226014, U.P., India.
概括
深度学习 (DL) 显著提高了磁共振成像 (MRI) 对于前列腺癌 (PC) 检测和分层. 这次审查强调了DL的重点.
科学领域:
- 医学成像分析分析 医学成像分析
- 人工智能在瘤学中的应用
- 诊断准确性的研究研究.
背景情况:
- 前列腺癌 (PC) 诊断严重依赖于成像.
- 磁共振成像 (MRI) 是PC的一个关键模式.
- 深度学习 (DL) 提供了改善诊断性能的潜力.
研究的目的:
- 评估将DL与MRI整合到PC检测和分层的影响.
- 评估DL在PC中提供的诊断性能改进.
- 审查当前的应用程序,并确定研究缺口.
主要方法:
- 在PubMed. 的系统文献搜索.
- 使用QUADAS-2工具进行质量评估.
- 遵守CLAIM和PRISMA指南的要求.
主要成果:
- 分析了29篇文章和17954名参与者.
- 对CLAIM指导方针的遵守率中位数为61.90%.
- 具有明显扩散系数 (ADC) 的T2加权成像 (T2WI) 和扩散加权成像 (DWI) 是常见的输入;过渡区分析有限.
结论:
- DL显示了快速,灵敏,特定和强大的PC检测和分层的前景.
- 未来的应用包括提高DWI质量,开发先进的DL模型,以及创建新的诊断工具.
- DL与MRI的整合可以改善前列腺癌的临床决策.
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