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PhD Defense - On the development of photoacoustic imaging biomarkers of cancer treatment response

Tuesday, May 19, 2020 - 10:00
Zoom presentation

Presenter: Eno Hysi

Supervisor: Dr. Michael Kolios


This dissertation describes the development of functional and structural photoacoustic (PA) imaging biomarkers that can be used to monitor cancer treatment response and potentially predict treatment outcome. An imaging method that can indicate individualized treatment success could improve therapeutic outcome. Assessing the effectiveness of therapies as early as possible may spare the patient from unnecessary treatments and save precious clinical resources. In order for PA imaging to enter mainstream radiology and become a treatment monitoring tool, rigorous development of biomarkers that are easy-to-use and representative of the treatment-induced changes in the tumor microenvironment are needed. In this work, I have developed imaging biomarkers that rely on the analysis of the radiofrequency signals in acoustic resolution PA imaging. Specifically, I show through simulations and experiments that biomarkers sensitive to the size, number density and spacing of tumor blood vessels can be extracted through time and frequency domain analysis of PA signals. This information is encoded in the speckle that forms during diffuse optical illumination, which was previously thought to be noise. Moreover, I demonstrate that PA imaging can detect the response of a thermosensitive liposome by measuring a >10% drop in the oxygenation of the tumor as early as 30 minutes post-treatment. This change in oxygenation is due to vascular disruption, a phenomenon that can be detected through frequency analysis of the PA signals. The spectral slope parameter decreases by as much as 73% in 2 hours post-treatment and can be used to differentiate alongside the oxygenation biomarker between responders and non-responders. Lastly, I demonstrate that these PA imaging biomarkers correlate well with the histologically measured biophysical changes of two novel, bubble-based cancer treatments. In this dissertation, PA imaging biomarkers for cancer treatment monitoring are developed, advancing the modality towards clinical translation.

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