Longitudinal quantitative MRI detects heterogeneity in response to radiation therapy within prostate cancer
Motivation: Tumour heterogeneity can result in a spatial variation in response to radiation. While quantitative MRI (qMRI) shows promise in monitoring treatment response in prostate cancer (PCa), there remains an untapped potential for spatial response mapping to detect radioresistant tumour sub-regions suitable for early salvage therapy. Goal(s): To assess the feasibility of treatment response mapping in a post-radiation therapy longitudinal qMRI PCa dataset. Approach: Longitudinal qMRI parameter maps of 16 PCa patients were analysed using voxel-wise and region of interest (ROI) approaches. Results: Voxel-wise analysis identified heterogeneity in treatment-related changes within PCa that would otherwise be concealed in ROI-based assessments. Impact: Voxel-wise analysis of longitudinal qMRI parameter maps can detect radiation treatment response in sub-volumes of prostate cancer. Reliable detection and localisation of early treatment response provides an opportunity for adaptive or salvage therapies of treatment-resistant tumours.
Authors
- Annette Haworth (ORCID: https://orcid.org/0000-0003-1382-7747)
- Mark Sidhom (ORCID: https://orcid.org/0000-0002-1679-1607)
- Jarad Martin (ORCID: https://orcid.org/0000-0002-4929-0278)
- Sirisha Tadimalla (ORCID: https://orcid.org/0000-0003-2054-055X)
- Lois Holloway (ORCID: https://orcid.org/0000-0003-4337-2165)
- Niluja Thiruthaneeswaran (ORCID: https://orcid.org/0000-0002-0253-0895)
- Sandra Turner (ORCID: https://orcid.org/0000-0002-7669-8316)
- Yufeng Wang (ORCID: https://orcid.org/0000-0002-4576-1984)
- Amy Hayden
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/10.58530/2024/0269
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00