通过集成MRI和蛋白质学,基于深度学习的NAC响应的多模式预测
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在PubMed上查看摘要
概括
此摘要是机器生成的。这项研究开发了一种新的MRI-proteomics深度学习框架,用于预测局部晚期直肠癌 (LARC) 的新辅助化疗 (NAC) 反应. 综合模型准确地确定了可能受益于NAC的患者,改善了治疗策略.
科学领域
- 癌症学
- 放射学
- 蛋白质组学
- 人工智能
背景情况
- 局部发达的直肠癌 (LARC) 对新辅助化疗 (NAC) 的反应不同,导致治疗延迟和无反应者的毒性.
- 目前的MRI和蛋白质组学方法由于单模性质而提供有限的预测准确性和生物洞察力.
- 整合MRI的空间信息与蛋白质组学的分子数据对于更好的预测至关重要.
研究的目的
- 开发和验证综合MRI和蛋白质组学的多式深度学习框架,用于预测LARC中的NAC反应.
- 为了提高个性化治疗选择的预测准确度,超越单模式方法.
- 确定与NAC反应相关的关键成像和分子特征.
主要方法
- 对274名用NAC治疗的LARC患者进行了回顾性分析.
- 一个深度学习框架结合了蛋白质组学的图形神经网络 (包含路径和网络) 和MRI T2WI的3D ResNet.
- 一个LightGBM分类器整合了具有临床特征的多式数据,使用缺失值的零归算.
主要成果
- 综合多式模式在试验组中实现了优异的NAC反应预测,AUC为0.828.
- 多模态方法显著优于单模态MRI (ΔAUC+0.109) 和蛋白质组模型 (ΔAUC+0.125).
- 与周围层异质性相关的MRI特征是关键预测因素,与10个已识别的化学抵抗蛋白一起.
结论
- 一个新的MRI-proteomics深度学习框架准确地预测了LARC中的NAC反应.
- 与单一模式方法相比,这种综合方法提供了更强的预测能力和生物洞察力.
- 该框架支持个性化治疗策略和器官保存在LARC管理中.
相关概念视频
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.