Dual deep learning approach for non-invasive renal tumour subtyping with VERDICT-MRI

基于双重深度学习的非侵入性肾肿瘤亚型分类方法(VERDICT-MRI)

阅读:1

Abstract

Renal cell carcinomas (RCCs) have multiple subtypes that are difficult to distinguish using imaging alone. This study characterises renal tumour microstructure using diffusion MRI (dMRI) and the Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours (VERDICT)-MRI framework. Patients were prospectively recruited from the RIM trial (ClinicalTrials.gov: NCT07173140, 20/11/2024). Fourteen patients with 17 renal tumours (including benign and various RCC subtypes) underwent dMRI using nine b-values (0-2500 s/mm²). A three-compartment VERDICT model was fitted with a self-supervised neural network. Compared to simpler dMRI models, VERDICT more accurately captured the diffusion data in tumour and healthy tissue. VERDICT revealed significant differences in intracellular volume fraction between cancerous and normal tissue, and in vascular volume fraction between vascular and non-vascular regions. A feature selection method identified a reduced 4 b-value protocol (b = [70, 150, 1000, 2000]), cutting scan time by over 30 min, enabling more efficient imaging in larger cohorts.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。