Improving tissue-type imaging of prostate cancer by combining magnetic-resonance spectroscopy and ultrasound spectral parameters

Abstract

This study aims to improve existing prostate tissue-type imaging methods by utilizing independent tissue properties sensed by MR and US. An artificial-neural-network classifier was trained using the blood PSA level and ultrasonic spectral parameters of echo signals derived from biopsied regions of 64 patients. Biopsy-core histology was used as the gold standard. Classifier performance was assessed using ROC analysis. We generated tissue-type images (TTIs) using a look-up table that returned cancer-likelihood scores for spectral parameter and PSA combinations. We assessed the feasibility of integrating MR and US parameters to improve classification. 3-D renderings of the prostate from transverse MR and US scans were generated from data acquired preoperatively. The 3-D rendering obtained from MR data showed that the prostate was considerably distorted (flattened) by the large MR endorectal probe compared to the distortion caused by the smaller US endorectal probe. The MR 3-D rendering was warped successfully to match the US volume. Warping successfully compensated for the deformations introduced during scanning. This suggests that accurate coregistration of 3-D data acquired using MR and US is possible. Such coregistration is essential for developing an improved classifier based on MR and US parameters.