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双重深度学习方法用于使用VERDICT-MRI进行非侵入性脏瘤亚型化

Snigdha Sen¹, Lorna Smith², Lucy Caselton³

¹UCL Hawkes Institute, 90 High Holborn, London, UK. snigdha.sen.20@ucl.ac.uk.

|January 6, 2026

概括

这项研究使用血管,细胞外和限制扩散用于瘤细胞计 (VERDICT) -MRI来表征脏瘤. VERDICT-MRI准确地区分组织类型,并使更快的成像协议提高效率.

科学领域:

放射学放射学是指放射学
医疗成像医学成像
在瘤学瘤学.

背景情况:

细胞癌 (RCCs) 呈现出各种各样的亚型,难以通过成像来区分.
准确地描述瘤微观结构对于诊断和治疗计划至关重要.

研究的目的:

通过扩散MRI (dMRI) 和VERDICT-MRI框架来表征瘤微观结构.
为了评估与更简单的dMRI模型相比,VERDICT-MRI的准确性.
开发一个更有效的dMRI协议用于临床使用.

主要方法:

从RIM试验中招募14名患有17种脏瘤 (包括RCC亚型和良性瘤) 的患者.
使用9个b值 (0-2500秒/毫米2) 获取dMRI数据.
使用自主监督的神经网络,拟合一个三部分的VERDICT模型.

主要成果:

与更简单的dMRI模型相比,VERDICT-MRI在瘤和健康脏组织中捕获扩散数据的准确性更高.
在癌症和正常组织之间观察到细胞内体积分数的显著差异.
在瘤内的血管和非血管区域之间发现了血管体积分数的显著差异.
一个减少的4b值协议 (b=[70,150,1000,2000]) 被确定,减少扫描时间超过30分钟.

结论:

VERDICT-MRI提供了更准确的脏瘤微观结构的特征.
这种先进的成像技术可以根据扩散特性区分癌症和正常组织.
优化,降低b值的协议提高成像效率,促进其在更大的患者队列和临床环境中的应用.

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