Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

Peiran Chen; Simona Turco; Yao Wang; Auke Jager; Gautier Daures; Hessel Wijkstra; Wim Zwart; Pintong Huang; Massimo Mischi

doi:10.1016/j.ultrasmedbio.2024.04.007

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

Ultrasound Med Biol. 2024 May 10:S0301-5629(24)00179-0. doi: 10.1016/j.ultrasmedbio.2024.04.007. Online ahead of print.

Authors

Peiran Chen¹, Simona Turco², Yao Wang³, Auke Jager⁴, Gautier Daures⁵, Hessel Wijkstra⁶, Wim Zwart⁵, Pintong Huang³, Massimo Mischi²

Affiliations

¹ Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands. Electronic address: p.chen1@tue.nl.
² Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
³ Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China.
⁴ Department of Urology, Amsterdam University Medical Centers, Amsterdam, Netherlands.
⁵ Angiogenesis Analytics, JADS Venture Campus, Netherlands.
⁶ Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands; Department of Urology, Amsterdam University Medical Centers, Amsterdam, Netherlands.

PMID: 38734528
DOI: 10.1016/j.ultrasmedbio.2024.04.007

Abstract

Objectives: To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes.

Methods: After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC).

Results: Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value < 0.05) using the Gradient Boosting classifier.

Conclusions: Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.

Keywords: Computer-assisted diagnosis; Dynamic contrast-enhanced ultrasound; Multiparametric ultrasound; Prostate cancer; Ultrasound shear-wave elastography.