Ultrasound-Based Predication of Prostate Cancer in MRI-guided Biopsy

In this paper, we report an in vivo clinical feasibility study for ultrasound-based detection of prostate cancer in MRI selected biopsy targets. Methods: Spectral analysis of a temporal sequence of ultrasound RF data reflected from a fixed location in the tissue results in features that can be used for separating cancerous from benign biopsies. Data from 18 biopsy cores and their respective histopathology are used in an innovative computational framework, consisting of unsupervised and supervised learning, to identify and verify cancer in regions as small as 1 mm $$\times $$ 1 mm. Results: In leave-one-subject-out cross validation experiments, an area under ROC of 0.91 is obtained for cancer detection in the biopsy cores. Cancer probability maps that highlight the predicted distribution of cancer along the biopsy core, also closely match histopathology. Our results demonstrate the potential of the RF time series to assist patient-specific targeting during prostate biopsy.