Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study
Journal article, 2019

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.

convolutional neural network

prostatic neoplasms

diagnostic imaging

choline

agreement

positron emission tomography

Author

Mike A. Mortensen

Odense Universitetshospital

University of Southern Denmark

Pablo Borrelli

Sahlgrenska University Hospital

M. H. Poulsen

Odense Universitetshospital

Oke Gerke

Odense Universitetshospital

Olof Enqvist

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Johannes Ulén

Eigenvision AB

E. Tragardh

Skåne University Hospital

Lund University

Caius Constantinescu

Odense Universitetshospital

L. Edenbrandt

Sahlgrenska University Hospital

Lars Lund

University of Southern Denmark

Odense Universitetshospital

P. F. Hoilund-Carlsen

University of Southern Denmark

Odense Universitetshospital

Clinical Physiology and Functional Imaging

1475-0961 (ISSN)

Vol. 39 6 399-406

Subject Categories

Urology and Nephrology

Radiology, Nuclear Medicine and Medical Imaging

Medical Image Processing

DOI

10.1111/cpf.12592

PubMed

31436365

More information

Latest update

11/10/2019