Machine learning for multiparametric ultrasound classification of prostate cancer using B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

R.R. Wildeboer, Christophe K. Mannaerts, R.J.G. van Sloun, H. Wijkstra, G. Salomon, M. Mischi

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

Abstract

The diagnosis of prostate cancer (PCa) is still based on systematic biopsy, but is increasingly developing towards an imaging-driven approach. In particular, multiparametric magnetic resonance imaging (MRI) is receiving increasing attention over the last few years. In light of MRI-related issues concerning costs, practicality, and availability, we investigate a multiparametric ultrasound (mpUS) approach. We propose and test a machine-learning-based strategy that automatically combines B-mode ultrasound, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) features. To this end, automatic zonal segmentation by deep learning, model-based feature estimation (related to contrast-agent perfusion and dispersion), radiomic feature extraction, and a random-forest-based pixel-wise classification were combined. The method was trained and validated against histopathologically-confirmed benign and malignant regions of interest in 48 PCa patients, in a leave-one-patient-out cross-correlation fashion. The mpUS classification algorithm yielded a region-wise area under the Receiver Operating Characteristics (ROC) curve of 0.75 and 0.90 for PCa and significant (i.e., Gleason ≥4+3) PCa, respectively. In comparison, the best-performing single parameter (i.e., DCE-US-based contrast velocity) reached a performance of 0.69 and 0.76 in terms of the ROC curve area. In particular the combination of perfusion-, dispersion-, and elasticity-related features were favored in the classification. Even though validation on a larger patient group is required, we have demonstrated the technical feasibility of automatic mpUS for PCa localization. Further development of mpUS might lead to a valuable clinical option for robust, ultrasound-driven PCa diagnosis.

Original languageEnglish
Title of host publication2019 IEEE International Ultrasonics Symposium, IUS 2019
Place of PublicationPiscataway
PublisherIEEE Computer Society
Pages1902-1905
Number of pages4
ISBN (Electronic)9781728145969
DOIs
Publication statusPublished - Oct 2019
Event2019 IEEE International Ultrasonics Symposium, IUS 2019 - Glasgow, United Kingdom
Duration: 6 Oct 20199 Oct 2019

Conference

Conference2019 IEEE International Ultrasonics Symposium, IUS 2019
CountryUnited Kingdom
CityGlasgow
Period6/10/199/10/19

Keywords

  • Dynamic Contrast-Enhanced Ultrasound
  • Machine Learning
  • Multipara-metric Ultrasound
  • Prostate Cancer
  • Shear-Wave Elastography

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