Abstract
Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to directly model and exploit temporal aspects of temporal enhanced ultrasound (TeUS) for tissue characterization, which improves malignancy prediction. We employ a probabilistic-temporal framework, namely, hidden Markov models (HMMs), for modeling TeUS data obtained from PCa patients. We distinguish malignant from benign tissue by comparing the respective log-likelihood estimates generated by the HMMs. We analyze 1100 TeUS signals acquired from 12 patients. Our results show improved malignancy identification compared to previous results, demonstrating over 85% accuracy and AUC of 0.95. Incorporating temporal information directly into the models leads to improved tissue differentiation in PCa. We expect our method to generalize and be applied to other types of cancer in which temporal-ultrasound can be recorded.
Highlights
Prostate cancer (PCa) is the most commonly diagnosed form of cancer in men, second only to skin cancer
For each combination of N and M, the structure of the hidden Markov models (HMMs) are learned through 12 cross-validation iterations, where in each iteration the region of interest (ROI) of one of the 12 patients are left-out for testing, while the ROIs of all other patients are used for training
We introduced stochastic models to improve the detection and stratification of PCa using temporal enhanced ultrasound (TeUS), an innovative ultrasound-based imaging technology
Summary
Prostate cancer (PCa) is the most commonly diagnosed form of cancer in men, second only to skin cancer. TRUS-guided biopsies often lead to a high rate (~ 40%) of false negatives for cancer diagnosis.[27]. Extensive heterogeneity in morphology and pathology of PCa are challenging factors for accurate diagnosis and grading of the disease.[4] While improved PCa screening has reduced mortality rates by 45% over the past two decades,[5,8] inaccurate diagnosis and grading lead to an increase in repeat biopsies, over-diagnosis and over-treatment.[10,16] Overaggressive treatment of PCa patients results in a decline in their quality of life
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