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

Author

Eirini Polymeri

University of Gothenburg

Sahlgrenska University Hospital

M. Sadik

Sahlgrenska University Hospital

R. Kaboteh

Sahlgrenska University Hospital

Pablo Borrelli

Sahlgrenska University Hospital

Olof Enqvist

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

Johannes Ulén

Eigenvision AB

M. Ohlsson

Halmstad University

E. Tragardh

Lund University

M. H. Poulsen

Odense Universitetshospital

J. Simonsen

Odense Universitetshospital

Poul Flemming Hoilund-Carlsen

Odense Universitetshospital

Åse (Allansdotter) Johnsson

University of Gothenburg

Sahlgrenska University Hospital

L. Edenbrandt

Sahlgrenska University Hospital

University of Gothenburg

Clinical Physiology and Functional Imaging

1475-0961 (ISSN)

Vol. 40 2 106-113

Subject Categories

Urology and Nephrology

Radiology, Nuclear Medicine and Medical Imaging

Medical Image Processing

DOI

10.1111/cpf.12611

PubMed

31794112

More information

Latest update

4/23/2020