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Ultrasound in Medicine & Biology
Article . 2024 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Ultrasound in Medicine & Biology
Article . 2024
License: CC BY
Data sources: Pure Amsterdam UMC
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Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

Authors: Chen, Peiran; Turco, Simona; Wang, Yao; Jager, Auke; Daures, Gautier; Wijkstra, Hessel; Zwart, Wim; +2 Authors

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

Abstract

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.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).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.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

Male, Computer-assisted diagnosis, Biopsy, Elasticity Imaging Techniques/methods, SDG 3 – Goede gezondheid en welzijn, Imaging, Imaging, Three-Dimensional, SDG 3 - Good Health and Well-being, Predictive Value of Tests, Three-Dimensional/methods, Dynamic contrast-enhanced ultrasound, Humans, Ultrasound shear-wave elastography, Aged, Ultrasonography, Prostate cancer, Prostate, Prostatic Neoplasms, Prostate/diagnostic imaging, Ultrasonography/methods, Middle Aged, Multiparametric ultrasound, Prostatic Neoplasms/diagnostic imaging, Elasticity Imaging Techniques

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
hybrid