Title
Multiparametric ultrasound and machine learning for prostate cancer localization
Author
Chen, Peiran (Eindhoven University of Technology)
Calis, Metin
Wijkstra, Hessel (Eindhoven University of Technology; Universiteit van Amsterdam)
Huang, Pintong (The Second Affiliated Hospital of Zhejiang University)
Hunyadi, Borbala (TU Delft Signal Processing Systems)
Mischi, Massimo (Eindhoven University of Technology)
Date
2022
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.
Subject
machine learning
multilinear singular value decomposition
prostate cancer
ultrasound
To reference this document use:
http://resolver.tudelft.nl/uuid:33b3ec43-787b-435c-8014-0b3cfd7df8e7
Publisher
European Signal Processing Conference, EUSIPCO
Embargo date
2023-04-24
ISBN
9789082797091
Source
30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
Event
30th European Signal Processing Conference, EUSIPCO 2022, 2022-08-29 → 2022-09-02, Belgrade, Serbia
Series
European Signal Processing Conference, 2219-5491, 2022-August
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2022 Peiran Chen, Metin Calis, Hessel Wijkstra, Pintong Huang, Borbala Hunyadi, Massimo Mischi