数字体积测活检芯通过深度学习改善前列腺癌的格里森分级
Ekaterina Redekop1, Mara Pleasure2, Zichen Wang1
1Department of Bioengineering, University of California, Los Angeles, USA.
ArXiv
|September 24, 2024
概括
这项研究引入了前列腺癌分级的体积核,通过使用3D组织数据改进了腺体分析. 这种新的方法在分类格里森等级组方面显著优于传统的二维方法.
科学领域:
- 数字病理学数字病理学
- 计算瘤学是一种计算瘤学.
- 医学中的人工智能
背景情况:
- 前列腺癌 (PCa) 诊断依赖于活检的组织学分级.
- 目前的深度学习方法分析2D组织切片,限制复杂的3D结构分析.
- 切片间的腺体形态变化阻碍了准确的分级.
研究的目的:
- 开发一种新的数字病理学数据源,即"体积核",用于改进前列腺癌分级.
- 实施基于注意力的多实例学习 (ABMIL) 框架,使用Gleason等级组 (GGG) 分类的3D数据.
- 通过在3D环境中保存组织形态来克服二维分析的局限性.
主要方法:
- 使用形态维护对齐框架,从连续切割的组织中创建"体积核心".
- 基于注意力的多实例学习 (ABMIL) 框架被训练在体积补丁的深度特征上.
- 一个经过修改的视频变压器与自主监督的预训练深度特征提取器处理了体积数据.
主要成果:
- 在ABMIL框架下,GGG分类的宏观平均AUC达到0.958.
- 该模型在所有五个GGG中都显示了0.671 F1得分,0.661精度和0.695回忆.
- 体积方法显著优于现有的二维基线方法.
结论:
- 与2D切片相比,体积核为前列腺癌组织学分析提供了优越的数据来源.
- 开发的ABMIL框架有效地使用3D病理数据对格里森等级组进行分类.
- 这种3D方法提高了前列腺癌自动分级的准确性,帮助诊断.
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