Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study

Research output: Contribution to journalArticle

Abstract

Aim: To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa). Methods: A convolutional neural network (CNN) was trained for automated measurements in 18F-choline (FCH) PET/CT scans obtained prior to radical prostatectomy (RP) in 45 patients with newly diagnosed PCa. Automated values were obtained for prostate volume, maximal standardized uptake value (SUVmax), mean standardized uptake value of voxels considered abnormal (SUVmean) and volume of abnormal voxels (Volabn). The product SUVmean × Volabn was calculated to reflect total lesion uptake (TLU). Corresponding manual measurements were performed. CNN-estimated data were compared with the weighted surgically removed tissue specimens and manually derived data and related to clinical parameters assuming that 1 g ≈ 1 ml of tissue. Results: The mean (range) weight of the prostate specimens was 44 g (20–109), while CNN-estimated volume was 62 ml (31–108) with a mean difference of 13·5 g or ml (95% CI: 9·78–17·32). The two measures were significantly correlated (r = 0·77, P<0·001). Mean differences (95% CI) between CNN-based and manually derived PET measures of SUVmax, SUVmean, Volabn (ml) and TLU were 0·37 (−0·01 to 0·75), −0·08 (−0·30 to 0·14), 1·40 (−2·26 to 5·06) and 9·61 (−3·95 to 23·17), respectively. PET findings Volabn and TLU correlated with PSA (P<0·05), but not with Gleason score or stage. Conclusion: Automated CNN segmentation provided in seconds volume and simple PET measures similar to manually derived ones. Further studies on automated CNN segmentation with newer tracers such as radiolabelled prostate-specific membrane antigen are warranted.

Details

Authors
  • Mike A. Mortensen
  • Pablo Borrelli
  • Mads Hvid Poulsen
  • Oke Gerke
  • Olof Enqvist
  • Johannes Ulén
  • Elin Trägårdh
  • Caius Constantinescu
  • Lars Edenbrandt
  • Lars Lund
  • Poul Flemming Høilund-Carlsen
Organisations
External organisations
  • Odense University Hospital
  • University of Southern Denmark
  • Sahlgrenska University Hospital
  • Chalmers University of Technology
  • Eigenvision AB
  • Skåne University Hospital
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Radiology, Nuclear Medicine and Medical Imaging

Keywords

  • agreement, choline, convolutional neural network, diagnostic imaging, positron emission tomography, prostatic neoplasms
Original languageEnglish
Pages (from-to)399-406
JournalClinical Physiology and Functional Imaging
Volume39
Issue number6
Early online date2019 Aug 22
Publication statusPublished - 2019
Publication categoryResearch
Peer-reviewedYes