Deep Learning-Based Quantification of PET/CT Prostate Gland Uptake: Association with Overall Survival.

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.

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 to 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 18 F-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 analyzed 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.

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.

Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival.

Clinical physiology and functional imaging. 2019 Dec 03 [Epub ahead of print]

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

Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden., Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden., Region Västra Götaland, Chalmers University of Technology, Department of Electrical Engineering, Gothenburg, Sweden., Eigenvision AB, Malmö, Sweden., Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Centre for Applied Intelligent Systems Research, Sweden., Department of Translational Medicine, Institute of Clinical Sciences, Lund University, Malmö, Sweden., Department of Urology, Odense University Hospital, Odense, Denmark., Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark.