Multiparametric dynamic contrast-enhanced ultrasound classification of prostate cancer

Although prostate cancer (PCa) is the most common non-cutaneous form of cancer among Western men, available diagnostic imaging methods are not yet sufficiently reliable to avoid systematic biopsy. In this work, we aim at improving the accuracy of transrectal dynamic contrast-enhanced ultrasonography (DCE-US) for PCa localization by combining local perfusion and dispersion parameters. To this end, ten of these parameters were extracted pixel-by-pixel from 45 DCE-US recordings distributed over 19 patients that were scheduled for radical prostatectomy. Based on 43 benign and 42 malignant histologically-confirmed regions of interest, we produced multiparametric maps using a Gaussian Mixture Model (GMM) algorithm. All possible combinations of one to four parameters were evaluated to select the most suitable subset of parameters. We also tested the GMM algorithm's ability to determine the classification confidence for each pixel and the impact of excluding low-confidence pixels from the images. An accuracy and negative predictive value of 81% and 83%, respectively, are obtained, which improved after pixel exclusion. Even though extended validation on a larger patient group is recommended, multiparametric DCE-US shows high potential in localizing PCa and might become an important tool for guiding targeted biopsy or planning of focal treatment.