多模式MALDI成像质谱仪用于改善黑色素瘤的诊断
Wanqiu Zhang1,2, Nathan Heath Patterson3,4, Nico Verbeeck2
1STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium.
PloS one
|May 31, 2024
概括
这项研究将深度学习与成像质谱学 (IMS) 和显微镜相结合,用于增强黑色素瘤分类. 与单独使用任何数据类型相比,多式联络方法显著提高了诊断准确性.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 计算病理学计算病理学
- 分子组织病理学分子病理学
背景情况:
- 图像质谱学 (IMS) 为组织病理学提供了丰富的空间分子数据.
- 传统的组织病理学依赖于显微镜,提供了在IMS分析中经常遗漏的关键形态信息.
- 区分黑色素瘤和瘤可能是具有挑战性的,仅仅使用组织病理学.
研究的目的:
- 开发和评估一个多模式分类管道,将深度学习从显微镜中提取的形态特征与IMS数据相结合.
- 评估这种多模式方法是否与单模式方法相比改善了黑色细胞新生病的分类.
主要方法:
- 使用预训练的神经网络开发了一个深度学习管道,以从组织病理图像中提取形态特征.
- 矩阵辅助激光脱吸/电离 (MALDI) IMS数据与331名患者的显微镜数据进行了整合.
- 多模式管道的性能与仅使用显微镜或IMS数据的单模式分类器进行了比较.
主要成果:
- 多式联网分类管道实现了最高的性能,ROC-AUC为0.968.
- 单模显微镜和单模 IMS 管道的 ROC-AUC 分别为 0.938 和 0.931.
- 对特征提取的深度学习方法不需要广泛的显微镜训练数据.
结论:
- 将基于深度学习的形态分析与IMS数据相结合,显著提高了黑色细胞新生病的分类.
- 这种多式模式的框架提供了一个易于应用的策略,以改善将显微镜和IMS结合在一起的实验中的分类.
- 管道利用预先训练的网络的能力使其适应各种研究环境.
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