Multiparametric ultrasound and machine learning for prostate cancer localization

Peiran Chen, Metin Calis, Hessel Wijkstra, Pintong Huang, Borbála Hunyadi, Massimo Mischi

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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6 Downloads (Pure)

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.

Original languageEnglish
Title of host publication30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages907-911
Number of pages5
ISBN (Electronic)978-90-827970-9-1
Publication statusPublished - 18 Oct 2022
Event30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
Duration: 29 Aug 20222 Sept 2022

Conference

Conference30th European Signal Processing Conference, EUSIPCO 2022
Abbreviated titleEUSIPCO 2022
Country/TerritorySerbia
CityBelgrade
Period29/08/222/09/22

Bibliographical note

Funding Information:
This work is supported in part by the research programme LOCATE with project number 15282, which is financed by the Dutch Research Council (NWO-TTW) and Holland High Tech with a PPS supplement for research and development in the Topsector HTSM. We would like to thank Angiogenesis Analytics for providing the infrastructure and the tools for running the experiments.

Keywords

  • machine learning
  • multilinear singular value decomposition
  • prostate cancer
  • ultrasound

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