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在双参数MRI中自动检测前列腺癌等级和慢性前列腺炎

Oleksii Bashkanov¹, Marko Rak¹, Anneke Meyer¹

¹Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany.

Computer methods and programs in biomedicine

|June 4, 2023

概括

这项研究表明,将前列腺炎纳入深度学习模型可以提高前列腺癌检测准确度. 精细粒度的前列腺炎分类有助于放射科医生在前列腺疾病的早期诊断.

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:

多参数磁共振成像 (mpMRI) 对于前列腺癌 (PCa) 查至关重要.
基于深度学习的计算机辅助诊断 (CAD) 工具帮助放射科医生解释复杂的MRI数据.
优化PCa检测方法需要检查最近的进展和实际培训考虑.

研究的目的:

通过深度学习评估用于多级前列腺癌检测的新方法.
在这种诊断背景下研究用于训练人工智能模型的实际策略.
为了确定双参数MRI分析的最佳配置.

主要方法:

利用了1647个活检证实发现的数据集,包括格里森得分和前列腺炎.
采用了3D nnU-Net架构,可以计算MRI数据的异构性.
探索了扩散权重成像 (DWI) 的最佳b值范围,模拟了数据增强的多式转移,并评估了在PCa检测中包括不同颗粒度 (粗,中,细) 的前列腺炎的影响.

主要成果:

一个最佳的模型配置,包括细粒状前列腺炎和一热编码 (OHE),在临床上显著的PCa (csPCa) 检测中实现了病变智能的FROC AUC为1.94和患者智能的ROC AUC为0.874.
将前列腺炎类纳入不同PCa类颗粒度的特异性提高了3-7%,每患者的错误阳性率为1.0.
序列和OHE输出配方进行了比较.

结论:

精细粒度分类,包括前列腺炎,显著有利于在双参数MRI中检测csPCa.
包括前列腺炎增加了特异性,并有助于早期诊断前列腺疾病.
这种方法提高了诊断质量和放射科医生对结果的解释性.

关键词:

计算机辅助诊断和检测.深度学习是一种深度学习.磁共振成像技术磁共振成像技术前列腺癌是什么意思前列腺癌是什么意思前列腺炎是一种前列腺炎.

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