训练集增强和协调使放射性模型能够检测肺癌的早期发病
Claire Huchthausen1, Menglin Shi1, Gabriel L A Sousa1
1University of Virginia.
Research square
|November 19, 2025
概括
使用放射学的机器学习模型可以从CT扫描中检测到早期的肺癌. 统一数据并增加与晚期结节的训练,提高了肺结节的检测准确度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 基于放射学的机器学习显示了使用CT扫描进行早期肺癌检测的前景.
- 挑战包括早期肺结节中的低恶性瘤率和可变图像采集.
- 由于这些局限性,现有的模型在临床适用性方面遇到了困难.
研究的目的:
- 开发用于早期肺癌检测的临床适用的放射学模型.
- 为应对低恶性瘤率和图像采集可变性的挑战.
- 调查数据协调和增强策略的影响.
主要方法:
- 在早期发育肺结节 (PNs) 的放射性特征上训练机器学习模型.
- 增强培训数据与后期开发的PN,并使用ComBat.Bat协调获取效应.
- 对比的协调策略:没有生物区别,具有共变量,并进行单独的协调.
主要成果:
- 在早期发展PNS上训练的初始模型显示出近乎偶然的表现.
- 与共变量协调的增强模型实现了0.72 (0.67-0.76) 的ROC-AUC.
- 单独协调的增强模型实现了0.69 (0.63-0.74) 的ROC-AUC,两者都显著改善.
结论:
- 数据协调和增强对于在早期肺癌检测中有效的放射学模型至关重要.
- 在协调过程中考虑生物差异可以显著提高模型性能.
- 这些发现支持开发放射学工具,用于使用常规查CT成像进行肺癌早期检测.
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