We aim to develop novel imaging biomarkers in the field of Positron Emission Tomography/Computed Tomography (PET/CT) and to generate empirical evidence for the purpose of analytical and clinical validation of the imaging biomarkers as indicators of prognosis and treatment efficacy in metastatic prostate cancer (PCa). Such validated imaging biomarkers would not only transform future clinical care but would also accelerate the development of therapies for PCa. The work will take the form of multidisciplinary collaboration between researchers from the fields of nuclear medicine, urology and mathematics, and the clinical validation will be performed as retrospective and prospective multicentric studies. The specific objectives of this project are:

  • To develop new imaging biomarkers for PET/CT images from metastatic PCa patients
  • To analytically validate the PET/CT imaging biomarkers
  • To clinically validate the PETC/CT imaging biomarkers in a predefined clinical context


  • Trägårdh E, Enqvist O, Ulén J, Hvittfeldt E, Garpered S, Belal SL, Bjartell A, Edenbrandt L. Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians. Eur J Nucl Med Mol Imaging. 2022 Apr 27. Online ahead of print.
  • Piri R, Nøddeskou-Fink AH, Gerke O, Larsson M, Edenbrandt L, Enqvist O, Høilund-Carlsen PF, Stochkendahl MJ. PET/CT imaging of spinal inflammation and microcalcification in patients with low back pain: A pilot study on the quantification by artificial intelligence-based segmentation. Clin Physiol Funct Imaging. 2022 Mar 23. Online ahead of print.
  • Borrelli P, Góngora JLL, Kaboteh R, Ulén J, Enqvist O, Trägårdh E, Edenbrandt L. Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer. EJNMMI Phys. 2022;9:6.
  • Paravastu SS, Theng EH, Morris MA, Grayson P, Collins MT, Maass-Moreno R, Piri R, Gerke O, Alavi A, Flemming Høilund-Carlsen P, Edenbrandt L, Saboury B. Artificial Intelligence in Vascular-PET: Translational and Clinical Applications. PET Clin. 2022;17:95-113.
  • Paravastu SS, Hasani N, Farhadi F, Collins MT, Edenbrandt L, Summers RM, Saboury B. Applications of Artificial Intelligence in 18F-Sodium Fluoride Positron Emission Tomography/Computed Tomography:: Current State and Future Directions. PET Clin. 2022;17:115-135.
  • Ying T, Borrelli P, Edenbrandt L, Enqvist O, Kaboteh R, Trägårdh E, Ulén J, Kjölhede H.Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer. Eur Radiol Exp. 2021;5:50.
  • Borrelli P, Kaboteh R, Enqvist O, Ulén J, Trägårdh E, Kjölhede H, Edenbrandt L. Artificial intelligence-aided CT segmentation for body composition analysis: a validation study. Eur Radiol Exp. 2021;5:11.
  • Borrelli P, Larsson M, Ulén J, Enqvist O, Trägårdh E, Poulsen MH, Mortensen MA, Kjölhede H, Høilund-Carlsen PF, Edenbrandt L. Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival. Clin Physiol Funct Imaging. 2021;41:62-67.
  • Saito S, Nakajima K, Edenbrandt L, Enqvist O, Ulén J, Kinuya S. Convolutional neural network-based automatic heart segmentation and quantitation in 123I-metaiodobenzylguanidine SPECT imaging. EJNMMI Res. 2021;11:105.
  • Borrelli P, Ly J, Kaboteh R, Ulén J, Enqvist O, Trägårdh E, Edenbrandt L. AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients. EJNMMI Phys. 2021;8:32.
  • Polymeri E, Kjölhede H, Enqvist O, Ulén J, Poulsen MH, Simonsen JA, Borrelli P, Trägårdh E, Johnsson ÅA, Høilund-Carlsen PF, Edenbrandt L. Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients. Scand J Urol. 2021;55:427-433.
  • Sadik M, López-Urdaneta J, Ulén J, Enqvist O, Krupic A, Kumar R, Andersson PO, Trägårdh E. Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin's lymphoma patients staged with FDG-PET/CT. Sci Rep. 2021;11:10382.
  • Piri R, Edenbrandt L, Larsson M, Enqvist O, Nøddeskou-Fink AH, Gerke O, Høilund-Carlsen PF. Aortic wall segmentation in 18F-sodium fluoride PET/CT scans: head-to-head comparison of artificial intelligence-based versus manual segmentation. J Nucl Cardiol 2021 May 12.
  • Piri R, Edenbrandt L, Larsson M, Enqvist O, Skovrup S, Iversen KK, Saboury B, Alavi A, Gerke O, Høilund-Carlsen PF. "Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison. J Nucl Cardiol. 2021 Aug 12.
  • Saboury B, Edenbrandt L, Piri R, Gerke O, Werner T, Arbab-Zadeh A, Alavi A, Høilund-Carlsen PF. Alavi-Carlsen Calcification Score (ACCS): A Simple Measure of Global Cardiac Atherosclerosis Burden. Diagnostics (Basel). 2021;11:1421.
  • Høilund-Carlsen PF, Piri R, Gerke O, Edenbrandt L, Alavi A. Assessment of Total-Body Atherosclerosis by PET/Computed Tomography. PET Clin. 2021;16:119-128.
  • Edenbrandt L, Borrelli P, Ulén J, Enqvist O, Trägårdh E Automated analysis of PSMA-PET/CT studies using convolutional neural networks.. medRxiv 2021.03.03.21252818
  • RECOMIA - a cloud-based platform for artificial intelligence research in nuclear medicine and radiology. Trägårdh E, Borrelli P, Kaboteh R, Gillberg T, Ulén J, Enqvist O, Edenbrandt L. EJNMMI Phys. 2020:4;7:51.
  • Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival. Polymeri E, Sadik M, Kaboteh R, Borrelli P, Enqvist O, Ulén J, Ohlsson M, Trägårdh E, Poulsen MH, Simonsen JA, Hoilund-Carlsen PF, Johnsson ÅA, Edenbrandt L. Clin Physiol Funct Imaging. 2020;40:106-113.
  • Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases. Lindgren Belal S, Sadik M, Kaboteh R, Enqvist O, Ulén J, Poulsen MH, Simonsen J, Høilund-Carlsen PF, Edenbrandt L, Trägårdh E. Eur J Radiol 2019;113:89-95.
  • Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study. Mortensen MA, Borrelli P, Poulsen MH, Gerke O, Enqvist O, Ulén J, Trägårdh E, Constantinescu C, Edenbrandt L, Lund L, Høilund-Carlsen PF. Clin Physiol Funct Imaging. 2019:39;399-406.
  • The use of a proposed updated EARL harmonization of 18F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations. Ly J, Minarik D, Edenbrandt L, Wollmer P, Trägårdh E. EJNMMI Res. 2019;9:65.
  • Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG‐PET/CT in Hodgkin and non‐Hodgkin lymphomas. Sadik M, Lind E, Polymeri E, Enqvist O, Ulen J, Trägårdh E. Clin Physiol Funct Imaging. 2019;39:78-84.
  • 3D skeletal uptake of 18F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer. Lindgren Belal S, Sadik M, Kaboteh R, Hasani N, Enqvist O, Svärm L, Kahl F, Simonsen J, Poulsen MH, Ohlsson M, Høilund-Carlsen PF, Edenbrandt L, Trägårdh E. EJNMMI Res 2017;7:1.