Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study
Artikel i vetenskaplig tidskrift, 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

Författare

Mike A. Mortensen

Odense Universitetshospital

Syddansk Universitet

Pablo Borrelli

Sahlgrenska universitetssjukhuset

M. H. Poulsen

Odense Universitetshospital

Oke Gerke

Odense Universitetshospital

Olof Enqvist

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Johannes Ulén

Eigenvision AB

E. Tragardh

Skånes universitetssjukhus (SUS)

Lunds universitet

Caius Constantinescu

Odense Universitetshospital

L. Edenbrandt

Sahlgrenska universitetssjukhuset

Lars Lund

Syddansk Universitet

Odense Universitetshospital

P. F. Hoilund-Carlsen

Syddansk Universitet

Odense Universitetshospital

Clinical Physiology and Functional Imaging

1475-0961 (ISSN)

Vol. 39 6 399-406

Ämneskategorier

Urologi och njurmedicin

Radiologi och bildbehandling

Medicinsk bildbehandling

DOI

10.1111/cpf.12592

PubMed

31436365

Mer information

Senast uppdaterat

2019-11-10