In this article, we describe a system for detecting dominant prostate tumors, based on a combination of features extracted from a novel multi-parametric quantitative ultrasound elastography technique.
The performance of the system was validated on a data-set acquired from n = 10 patients undergoing radical prostatectomy. Multi-frequency steady-state mechanical excitations were applied to each patient's prostate through the perineum and prostate tissue displacements were captured by a transrectal ultrasound system. 3D volumetric data including absolute value of tissue elasticity, strain and frequency-response were computed for each patient. Based on the combination of all extracted features, a random forest classification algorithm was used to separate cancerous regions from normal tissue, and to compute a measure of cancer probability. Registered whole mount histopathology images of the excised prostate gland were used as a ground truth of cancer distribution for classifier training. An area under receiver operating characteristic curve of 0.82 +/- 0.01 was achieved in a leave-one-patient-out cross validation. Our results show the potential of multi-parametric quantitative elastography for prostate cancer detection for the first time in a clinical setting, and justify further studies to establish whether the approach can have clinical use.
Mohareri O, Ruszkowski A, Lobo J, Ischia J, Baghani A, Nir G, Eskandari H, Jones E, Fazli L, Goldenberg L, Moradi M, Salcudean S. Are you the author?
Reference: Med Image Comput Comput Assist Interv. 2014;17(Pt 1):561-8.