VERDICT-MRIを用いた非侵襲的腎腫瘍亜分類のためのデュアル深層学習アプローチ
Snigdha Sen1, Lorna Smith2, Lucy Caselton3
1UCL Hawkes Institute, 90 High Holborn, London, UK. snigdha.sen.20@ucl.ac.uk.
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.
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Imaging Studies IV: Magnetic Resonance Imaging
Imaging Studies I: Kidney, Ureter, and Bladder Studies
