Dual Deep Learning Approach for Non-invasive Renal Tumour Subtyping with VERDICT-MRI
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908321721614336 |
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| author | Sen, Snigdha Smith, Lorna Caselton, Lucy Clemente, Joey Tran, Maxine Punwani, Shonit Atkinson, David Hesketh, Richard L Panagiotaki, Eleftheria |
| author_facet | Sen, Snigdha Smith, Lorna Caselton, Lucy Clemente, Joey Tran, Maxine Punwani, Shonit Atkinson, David Hesketh, Richard L Panagiotaki, Eleftheria |
| contents | This work aims to characterise renal tumour microstructure using diffusion MRI (dMRI); via the Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours (VERDICT)-MRI framework with self-supervised learning. Comprehensive datasets were acquired from 14 patients with 15 biopsy-confirmed renal tumours, with nine b-values in the range b=[0,2500]s/mm2. A three-compartment VERDICT model for renal tumours was fitted to the dMRI data using a self-supervised deep neural network, and ROIs were drawn by an experienced uroradiologist. An economical acquisition protocol for future studies with larger patient cohorts was optimised using a recursive feature selection approach. The VERDICT model described the diffusion data in renal tumours more accurately than IVIM or ADC. Combined with self-supervised deep learning, VERDICT identified significant differences in the intracellular volume fraction between cancerous and normal tissue, and in the vascular volume fraction between vascular and non-vascular. The feature selector yields a 4 b-value acquisition of b = [70,150,1000,2000], with a duration of 14 minutes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07246 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual Deep Learning Approach for Non-invasive Renal Tumour Subtyping with VERDICT-MRI Sen, Snigdha Smith, Lorna Caselton, Lucy Clemente, Joey Tran, Maxine Punwani, Shonit Atkinson, David Hesketh, Richard L Panagiotaki, Eleftheria Image and Video Processing This work aims to characterise renal tumour microstructure using diffusion MRI (dMRI); via the Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours (VERDICT)-MRI framework with self-supervised learning. Comprehensive datasets were acquired from 14 patients with 15 biopsy-confirmed renal tumours, with nine b-values in the range b=[0,2500]s/mm2. A three-compartment VERDICT model for renal tumours was fitted to the dMRI data using a self-supervised deep neural network, and ROIs were drawn by an experienced uroradiologist. An economical acquisition protocol for future studies with larger patient cohorts was optimised using a recursive feature selection approach. The VERDICT model described the diffusion data in renal tumours more accurately than IVIM or ADC. Combined with self-supervised deep learning, VERDICT identified significant differences in the intracellular volume fraction between cancerous and normal tissue, and in the vascular volume fraction between vascular and non-vascular. The feature selector yields a 4 b-value acquisition of b = [70,150,1000,2000], with a duration of 14 minutes. |
| title | Dual Deep Learning Approach for Non-invasive Renal Tumour Subtyping with VERDICT-MRI |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2504.07246 |