ARFI and shear wave imaging of the prostate to delineate clinically-significant cancers

Abstract

Current prostate cancer screening methods involve non-targeted needle biopsies and the detection of clinically-insignificant lesions that receive excessive treatments, exposing patients to unnecessary adverse side effects and placing a burden on our health care systems. There is a strong clinical need for improved prostate imaging methods that are sensitive and specific for clinically-significant prostate cancer lesions to guide needle biopsies, target focal treatments, and improve overall patient outcomes. We have developed a 3D, in vivo Acoustic Radiation Force Impulse (ARFI) imaging and Shear Wave Elasticity Imaging (SWEI) system using a side-fire endorectal linear array with a custom rotation stage to collect prostate imaging volumes to identify regions of suspicion that should be targeted during biopsy procedures. We have validated that ARFI imaging can reliability identify regions of clinically-significant prostate cancer by comparing our imaging volumes with whole-mount histology in patients undergoing radical prostatectomy; however, there was opportunity to improve the diagnostic sensitivity and specificity by combining ARFI imaging data with SWEI. While ARFI images contain regions of relative contrast (decreased displacement) to delineate regions of suspicion, we hypothesized that SWEI could provide absolute shear wave speeds that could quantify the degree of suspicion of an identified lesion, with more aggressive lesions being stiffer and having higher shear wave speeds. In an ongoing clinical study of over 30 patients, we have demonstrated that the combination of ARFI and SWEI data can improve the diagnostic accuracy of clinically-significant lesion identification, while the inclusion of B-mode echogenicity was not found to improve diagnostic accuracy. More complex classification models to identify clinically-significant prostate cancer using ARFI and SWEI metrics are being developed using Linear Discriminant Analysis (LDA) and Support-Vector Machines (SVMs). These classification models will be further extended to explore the diagnostic value of additional quantitative ultrasound metrics derived from the raw radiofrequency (RF) data and other imaging methods commonly used in the diagnostic workup of prostate cancer patients, such as multiparametric magnetic resonance imaging (mp-MRI).