Multiparametric ultrasound and machine learning for prostate cancer localization

Abstract

A cost-effective, widely available, and practical diagnostic imaging tool for prostate cancer (PCa) localization is still lacking. Recently, the contrast-ultrasound dispersion imaging (CUDI) technique has been developed for PCa localization by quantifying dynamic contrast-enhanced ultrasound (DCE-US) acquisitions. Tissue stiffness is an additional PCa biomarker that can be quantified by ultrasound shear-wave elastography (SWE). In this work, a dedicated preprocessing of 3D DCE-US acquisitions was investigated by using multilinear singular value decomposition (MLSVD), aiming at improving the CUDI performance. Moreover, the diagnostic potential of a multiparametric ultrasound imaging approach combining 3D CUDI features with SWE tissue elasticity for clinically significant (cs)PCa localization was evaluated by comparison with the histopathological outcome of systematic biopsies. In this multiparametric approach, the performance of five classifiers was evaluated and compared for biopsy-region csPCa classification. The classification performance was assessed by the area under the Receiver Operating Characteristics curve (AUC) in a k-fold cross validation fashion comprising sequential floating forward selection of the features. The combination of CUDI features with MLSVD preprocessing and SWE elasticity yielded the best AUC=0.87 for csPCa localization. Our results suggest 3D multiparametric ultrasound imaging approach combing a dedicated preprocessing step to be a useful tool for PCa diagnostics.