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基于MRI的扩散加权成像和扩散曲解成像,使用深度学习分类器对清细胞细胞癌进行分级

Wenjing Zheng^1,2, Xin Luo³, Yangyingqiu Liu³

¹Department of Medical Imaging, Binzhou Medical University, Yantai, Shandong 264003, P.R. China.

Oncology letters

|September 8, 2025

概括

使用MRI扩散成像的深度学习 (DL) 模型准确地预测了清细胞细胞癌 (ccRCC) 等级. 这种非侵入性方法有助于为ccRCC患者做出个性化治疗决策.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

清细胞细胞癌 (ccRCC) 是细胞癌 (RCC) 最常见的亚型,约占病例的85%.
精确的手术前ccRCC病理分级对于指导治疗策略和预测患者结果至关重要.
目前的分级方法通常依赖于侵入性手术或术后分析,突出了对非侵入性预测工具的需求.

研究的目的:

为了验证深度学习 (DL) 模型的有效性,用于ccRCC病理分级的非侵入性,手术前预测.
评估DL算法的性能,使用扩散加权成像 (DWI) 和扩散曲解成像 (DKI) 的MRI序列.

主要方法:

一个DL算法被开发和训练使用DWI和DKIMRI序列从79个ccRCC患者 (40低级,39高级).
图像参数包括明显扩散系数 (ADC),轴向曲率 (Ka),分数异构,辐射曲率 (Kr) 和平均曲率 (MK) 被计算出来.
VGG-16模型是验证DL模型预测性能的骨干架构.

主要成果:

DL模型证明了ccRCC病理分级的准确预测.
在测试的参数中,平均MK获得了最高的准确度 (76.04%),精度 (81.48%),F1得分 (76.04%),和回忆率 (74.08%).
Kr和Ka也显示出有希望的预测值,表明了DKI参数的实用性.

结论:

建立的DL模型,利用MRI DWI和DKI序列,可以准确地预测ccRCC病理级别的非侵入性.
这种人工智能驱动的方法显示了改善手术前评估和指导ccRCC患者个性化治疗决策的巨大潜力.
进一步验证和整合到临床工作流程中可以提高细胞癌的患者管理和结果.

关键词:

清细胞细胞癌是什么?深度学习是一种深度学习.磁共振成像技术的使用无线电学 (radiomics) 是一种无线电学.

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