Prostate Cancer: Improved Tissue Characterization by Temporal Modeling of Radio-Frequency Ultrasound Echo Data.

Despite recent advances in clinical oncology, prostate cancer remains a major health concern in men, where current detection techniques still lead to both over- and under-diagnosis. More accurate prediction and detection of prostate cancer can improve disease management and treatment outcome. Temporal ultrasound is a promising imaging approach that can help identify tissue-specific patterns in time-series of ultrasound data and, in turn, differentiate between benign and malignant tissues. We propose a probabilistic-temporal framework, based on hidden Markov models, for modeling ultrasound time-series data obtained from prostate cancer patients. Our results show improved prediction of malignancy compared to previously reported results, where we identify cancerous regions with over 88 % accuracy. As our models directly represent temporal aspects of the data, we expect our method to be applicable to other types of cancer in which temporal-ultrasound can be captured.