Prostate Cancer Diagnosis Using Ultrasound Radio-Frequency Signals

Prostate cancer (PCa) is the most commonly diagnosed malignancy, and the second leading cancer-related cause of death in North American men. If diagnosed early, PCa can be managed with a 5-year relative survival rate above 95%. The current standard for PCa diagnosis involves ultrasound-guided core needle biopsy; however, the biopsy procedure is not scaled to individual patients due to the lack of sensitivity and specificity of conventional ultrasound images. Previously, a tissue typing approach was proposed that uses ultrasound RF time series acquired from a stationary transducer and issue position over a few seconds.


The goal is to evaluate the application of RF time series for in vivo cancer detection and for cancer treatment. I pursue this goal in the context of three experiments involving: i) prediction of cancer for in vivo radical prostatectomy cases, ii) prediction of cancer following prostate biopsy, and iii) prediction of changes to the tissue following interstitial ablation therapy. Finally, in simulation and controlled laboratory experiments, the rise of tissue temperature is explored as a potential source of tissue typing information in RF time series.


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