Advanced ultrasonic imaging of the prostate for guiding biopsies and for planning and monitoring therapy

Our studies are driven by the hypotheses that tissues of the prostate can be classified effectively based on spectrum-analysis parameters computed from ultrasonic radio-frequency echo signals in combination with clinical variables that are evaluated by neural networks and other non-linear methods. The classification of primary interest in these studies is cancerous vs. non-cancerous prostate tissues. Images based on these classification methods would be of clinical value for improving biopsy guidance and non-invasive evaluation of cancer for disease staging, treatment planning, and treatment monitoring. Our classifiers are trained using biopsy results as the gold standard and spectrum-analysis parameters and clinical variables (e.g., PSA value and patient age) as the inputs for tissue typing. ROC curves based on 1,019 biopsies consistently show ROC-curve areas of 0.80 or more for the methods under study compared to areas of 0.66 or less for levels of suspicion based on the B-mode appearance of matching biopsy scan planes. These curves imply that sensitivity improvements of more than 50% might be possible by incorporating these methods into prostate-image encoding. Initial clinical validation of biopsy-guidance efficacy have begun using tissue-classifying images generated in real time; preliminary results suggest that such images increase the likelihood of detecting cancers that are missed when biopsies are guided by conventional ultrasonic images. Parallel studies to incorporate these images into brachytherapy and IMRT planning procedures have been initiated