Deep learning-based quantification of PET/CT prostate gland uptake: association with overall survival

Research output: Contribution to journalArticle

Abstract

Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival.

Details

Authors
  • Eirini Polymeri
  • May Sadik
  • Reza Kaboteh
  • Pablo Borrelli
  • Olof Enqvist
  • Johannes Ulén
  • Mattias Ohlsson
  • Elin Trägårdh
  • Mads H. Poulsen
  • Jane A. Simonsen
  • Poul Flemming Hoilund-Carlsen
  • Åse A. Johnsson
  • Lars Edenbrandt
Organisations
External organisations
  • Sahlgrenska Academy
  • Sahlgrenska University Hospital
  • Chalmers University of Technology
  • Halmstad University
  • Odense University Hospital
  • Eigenvision AB
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Medical Image Processing
  • Urology and Nephrology

Keywords

  • artificial intelligence, convolutional neural network, objective quantification, prostatic neoplasms
Original languageEnglish
Pages (from-to)106-113
Number of pages8
JournalClinical Physiology and Functional Imaging
Volume40
Issue number2
Early online date2019 Dec 3
Publication statusPublished - 2020 Mar
Publication categoryResearch
Peer-reviewedYes