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在神经瘤学中基于人工智能的三种常见恶性瘤的分类:机器学习和深度学习方法的多机构比较

Girish Bathla¹, Durjoy Deb Dhruba², Neetu Soni³

¹Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA 52242, USA; Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55902, USA.

Journal of neuroradiology = Journal de neuroradiologie

|August 31, 2023

概括

机器学习 (ML) 和深度学习 (DL) 管道在分类像质母细胞瘤 (GBM) 这样的脑瘤方面表现相似. 准确分类的关键特征包括T1-对比增强 (T1-CE) 和周围部区域 (PTR) 面具.

科学领域:

神经成像是一种神经成像.
人工智能在医学中的应用
在瘤学瘤学.

背景情况:

准确分类初级中枢神经系统 (CNS) 瘤对于有效的治疗计划至关重要.
区分质母细胞瘤 (GBM),内转移性疾病 (IMD) 和初级中枢神经系统淋巴瘤 (PCNSL) 是一个重大的诊断挑战.
人工智能驱动的方法,特别是机器学习 (ML) 和深度学习 (DL),为提高诊断准确性提供了有前途的途径.

研究的目的:

将ML和DL管道在基于AI的GBM,IMD和PCNSL三类分类中的性能进行比较.
用ML和DL模型识别瘤分类的最佳成像特征和细分面具.

主要方法:

对502个培训案例和86个外部验证案例进行了回顾性分析.
处理了多参数MRI图像 (T1W,T2W,FLAIR,DWI,T1-CE),包括联合注册,无声化和正常化.
进行了增强瘤 (ET) 和周区域 (PTR) 的半自动3D细分.
使用各种ML管道和3D卷积神经网络 (3D-CNN) 用序列特定和组合面具评估性能,采用5倍嵌套交叉验证和与多类AUC的外部验证.

主要成果:

两种ML模型实现了高性能,AUC为0.885和0.878,分别使用T2-ET/T2-PTR和T1-CE-ET/FLAIR-PTR口罩.
性能最好的DL模型使用T1-CE-ET/T2-PTR和T1-CE-ET/ADC-ET/FLAIR-PTR面罩实现了0.854和0.851的AUC.
无论是ML和DL管道都在外部验证数据上表现出类似的分类性能.

结论:

在基于AI的GBM,IMD和PCNSL分类中,ML和DL管道表现出相似的有效性.
在四个表现最好的模型中,T1-对比增强 (T1-CE) 面具是三个模型的关键组件.
来自周口区域 (PTR) 的细分面具,特别是T2WI或FLAIR,是所有高性能模型的组成部分.

关键词:

人工智能的人工智能是人工智能.中枢神经系统淋巴瘤中枢神经系统淋巴瘤质母细胞瘤 (glioblastoma) 是一个转移转移转移转移转移无线电学 (Radiomics) 是一种无线电学.

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