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

Mike Allan Mortensen*, Pablo Borrelli, Mads Hvid Poulsen, Oke Gerke, Olof Enqvist, Johannes Ulén, Elin Trägårdh, Caius Constantinescu, Lars Edenbrandt, Lars Lund, Poul Flemming Høilund-Carlsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

11 Downloads (Pure)

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 (SUV max), mean standardized uptake value of voxels considered abnormal (SUV mean) and volume of abnormal voxels (Vol abn). The product SUV mean × Vol abn 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, Vol abn (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 Vol abn 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.

Original languageEnglish
JournalClinical Physiology and Functional Imaging
Volume39
Issue number6
Pages (from-to)399-406
ISSN1475-0961
DOIs
Publication statusPublished - Nov 2019

Keywords

  • agreement
  • choline
  • convolutional neural network
  • diagnostic imaging
  • positron emission tomography
  • prostatic neoplasms

Fingerprint Dive into the research topics of 'Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study'. Together they form a unique fingerprint.

  • Cite this