Applications of artificial intelligence to prostate multiparametric mri (Mpmri): Current and emerging trends

Michelle D. Bardis, Roozbeh Houshyar, Peter D. Chang, Alexander Ushinsky, Justin Glavis-Bloom, Chantal Chahine, Thanh Lan Bui, Mark Rupasinghe, Christopher G. Filippi, Daniel S. Chow

Research output: Contribution to journalReview articlepeer-review

20 Scopus citations

Abstract

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists’ accuracy and speed.

Original languageEnglish
Article number1204
JournalCancers
Volume12
Issue number5
DOIs
StatePublished - May 2020

Keywords

  • Artificial intelligence
  • Deep learning
  • Machine learning
  • Neural network
  • Prostate carcinoma
  • Prostate mpMRI

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