Sarcoma imaging: from improving clinical care towards decoding tumour phenotype
Multimodality imaging for personalising sarcoma care (MULTIPLE)
In this project, researchers at the Leiden University Medical Centre (LUMC) are investigating on how quantitative medical imaging can assist in personalising treatment of patients with soft tissue and bone cancers. Individualising treatment has a great potential to further maximise treatment effect and reduce unnecessary side effects and cost associated with these treatments. The LUMC is collaborating closely with Philips Healthcare in order to ensure that latest imaging technologies and analysis tools can be used in this research.
In clinical practice, selection of optimal treatment is difficult for these patients, given that there are more than 50 distinct types of sarcoma. Furthermore, as much as 20% of these patients are below 40 years. This emphasises the importance of saving patients from debilitating surgeries, employing strategies to operate sparingly whilst still removing the entire tumour, and avoid toxicity from ineffective chemo- and or radiotherapy. Employing personalised medicine for these patients has the potential to improve patient outcome and quality of life.
The researcher at the LUMC will investigate the value of quantitative medical imaging for measuring biological processes (e.g. glucose metabolism, water diffusion, perfusion) in the tumour to identify patients at risk of disease recurrence. Furthermore, the change in these characteristics over time will be correlated with effectiveness of chemo- and or radiotherapy.
The combined information from different imaging modalities (such as PET, CT, and MRI scans) will be used to create models that can predict clinical course of these patients. Improved knowledge whether patients benefit from specific treatments or require additional treatment before or after surgery. Eventually such models can be employed in clinical practice, where imaging data can provide an integrate role in improved clinical decision making.