Tumour mechanics and vascular fractality quantification via MR-Elastography in the context of liver metastasis from colorectal cancer

Motivation: Colorectal cancer is a major global cause of cancer-related deaths, often metastasizing to the liver. Standard treatment includes chemotherapy and anti-angiogenic therapy. Quantifying therapy efficacy remains a clinical challenge. Goal(s): We explore multifrequency MR-Elastography (MRE) for assessing vascular organization, using a murine liver metastasis model correlated with histopathology. Approach: The study used MRE imaging of murin liver metastasis model and the corresponding histopathology to analyze vascular organization (fractal dimension), by measuring Hurst index (H-index). Results: The H-index differs significantly between tumor and healthy liver tissue, with normal vasculature displaying a lower H-index compared to the tumoral tissue. Impact: Our in vivo elastography study demonstrates the organization of the vascular network by matching with histological findings. This innovative approach paves the way for non-invasive evaluation of treatments targeting tumor vessels, such as bevacizumab or FOLFOX.

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Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/3975
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Tumour mechanics and vascular fractality quantification via MR-Elastography in the context of liver metastasis from colorectal cancer

Valérie Vilgrain, Giacomo Annio, Samira Laouirem, Valérie Paradis et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

Tumour mechanics and vascular fractality quantification via MR-Elastography in the context of liver metastasis from colorectal cancer

Valérie Vilgrain, Giacomo Annio, Samira Laouirem, Valérie Paradis, Asma Boumaza, Gabrielle Mangin, Ralph Sinkus, Miguel Albuquerque, Jake Penney
article en

Abstract

Motivation: Colorectal cancer is a major global cause of cancer-related deaths, often metastasizing to the liver. Standard treatment includes chemotherapy and anti-angiogenic therapy. Quantifying therapy efficacy remains a clinical challenge. Goal(s): We explore multifrequency MR-Elastography (MRE) for assessing vascular organization, using a murine liver metastasis model correlated with histopathology. Approach: The study used MRE imaging of murin liver metastasis model and the corresponding histopathology to analyze vascular organization (fractal dimension), by measuring Hurst index (H-index). Results: The H-index differs significantly between tumor and healthy liver tissue, with normal vasculature displaying a lower H-index compared to the tumoral tissue. Impact: Our in vivo elastography study demonstrates the organization of the vascular network by matching with histological findings. This innovative approach paves the way for non-invasive evaluation of treatments targeting tumor vessels, such as bevacizumab or FOLFOX.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
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Radiomics and Machine Learning in Medical Imaging
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Tumour mechanics and vascular fractality quantification via MR-Elastography in the context of liver metastasis from colorectal cancer — Valérie Vilgrain, Giacomo Annio, et al. · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2024) | TGRS Research Map | TGRS