Deep learning-based quantification of PET/CT prostate gland uptake: association with overall survival
Artikel i vetenskaplig tidskrift, 2020

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.

convolutional neural network

objective quantification

prostatic neoplasms

artificial intelligence


Eirini Polymeri

Göteborgs universitet

Sahlgrenska universitetssjukhuset

M. Sadik

Sahlgrenska universitetssjukhuset

R. Kaboteh

Sahlgrenska universitetssjukhuset

Pablo Borrelli

Sahlgrenska universitetssjukhuset

Olof Enqvist

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Johannes Ulén

Eigenvision AB

M. Ohlsson

Högskolan i Halmstad

E. Tragardh

Lunds universitet

M. H. Poulsen

Odense Universitetshospital

J. Simonsen

Odense Universitetshospital

Poul Flemming Hoilund-Carlsen

Odense Universitetshospital

Åse (Allansdotter) Johnsson

Göteborgs universitet

Sahlgrenska universitetssjukhuset

L. Edenbrandt

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Clinical Physiology and Functional Imaging

1475-0961 (ISSN) 1475097x (eISSN)

Vol. 40 2 106-113


Urologi och njurmedicin

Radiologi och bildbehandling

Medicinsk bildbehandling





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