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基于深度学习的多任务全过程系统,用于自动诊断乳腺病变和支淋巴结转移的情况,从动态对比增强MRI进行歧视:一个多中心研究

Heng Zhou¹, Zhen Hua¹, Jing Gao²

¹School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China.

Journal of magnetic resonance imaging : JMRI

|July 27, 2023

概括

一个深度学习系统帮助放射科医生诊断乳腺病变和淋巴结转移,提高准确性. 这种人工智能工具提高了乳腺癌评估的诊断性能.

科学领域:

放射学和医学成像学医学成像学
医疗保健中的人工智能
在瘤学瘤学.

背景情况:

精确诊断乳腺病变和下淋巴结 (ALN) 转移至关重要,但严重依赖于放射科医生的专业知识.
当前的诊断方法可能是主观的和经验依赖的,导致潜在的变化.

研究的目的:

开发和评估基于深度学习的全过程系统 (DLWPS) 来进行全面的乳腺损伤分析.
该系统旨在改善细分,诊断乳腺病变,以及对ALN转移的歧视.

主要方法:

使用U-Net框架开发了一个DLWPS,用于细分的注意力和边缘特征提取,以及结合三个网络输出的分类模型.
该系统在1760名乳腺患者病例中使用3.0T动态对比增强 (DCE) -MRI进行了训练和验证.
放射科医生的诊断绩效与或没有DLWPS的协助,以及对生物见解的基因分析一起进行评估.

主要成果:

细分模型实现了0.828 (内部) 和0.813 (外部) 的高子相似系数 (DI).
DLWPS表现出强大的诊断性能,曲线下的面积 (AUC) 为乳腺病变的0.973 (内部) 和0.936 (外部),以及ALN转移的0.927 (内部) 和0.917 (外部).
随着DLWPS的协助,放射科医生一致性显著改善,从损伤的0.547到0.794,以及ALN转移的0.848到0.892.

结论:

该DLWPS显示显著的潜力,以提高放射科医生在诊断乳腺病变和歧视ALN转移的能力.
该系统的性能表明,它可以作为一种有价值的工具,以提高诊断准确性和乳腺癌评估的一致性.
基因分析确定了ALN转移与有氧电子运输和细胞质转化等途径之间的联系.

关键词:

轴心淋巴结的淋巴结发生了什么?生物基础生物基础乳腺病变的病变乳腺病变深度学习是一种深度学习.诊断诊断诊断诊断诊断诊断细分化细分化的细分化

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