Quantitative MRI for Monitoring Heterogeneous Radiotherapy Response in Soft-Tissue Sarcoma
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Embargo End Date
2025-10-24
ICR Authors
Authors
Thrussell, I
Document Type
Thesis or Dissertation
Date
2024-10-24
Date Accepted
Abstract
This work explores using quantitative MRI biomarkers to assess treatment-induced changes in Soft-Tissue Sarcomas (STS).
STS are rare heterogeneous tumours that develop in the connective tissues and current image-based treatment response evaluation techniques do not correlate well with histopathological analysis. There is a need for novel imaging biomarkers that can better represent the underlying biological characteristics of these tumours.
In this work a multiparametric, quantitative MRI (qMRI) protocol is developed allowing the extraction of multiple quantitative biomarkers from a single MR exam. In a dedicated clinical trial, STS patients treated by radiotherapy were examined using the qMRI protocol at three time points: before treatment, during early-treatment and post-treatment.
Several quantitative biomarkers were analysed from each imaging time point, including the ADC, FA, FF, MTR, EF, R1 and R2. Relationships between the biomarkers and how they change throughout treatment was assessed. Data from the histopathological clinical reports including sarcoma subtype and histopathological analysis (viable tumour percentage) were compared with the qmri biomarkers.
In addition, a new imaging technique, multi-frequency MR elastography, was developed for use in the clinical trial allowing for the extraction of another qMRI biomarker: wave speed. Phantom and volunteer studies were used to optimise the parameters for MRE within this patient cohort and initial patient measurements were taken.
An additional project which looked at radiomic features in retroperitoneal sarcomas (completed during the coronavirus lockdown) was also completed. The repeatability of radiomic features from ADC maps and the ability to change post-treatment was analysed using a previous patient cohort. An independent repeatable subset of radiomic features that change following treatment was identified as potential imaging biomarkers.
Citation
2024
DOI
Source Title
Publisher
Institute of Cancer Research (University Of London)
ISSN
eISSN
Collections
Research Team
Computational Imaging