NIMSA

AI-based analysis of PET/CT in Lung Cancer patients

Publications

• Borrelli P, Góngora JLL, Kaboteh R, Enqvist O, Edenbrandt L. Automated classification of PET-CT lesions in lung cancer: An independent validation study. Clin Physiol Funct Imaging. 2022;42:327.
• 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.
• 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.

PET/CT in Lymphoma

Publications

• Sadik M, Barrington SF, Ulén J, Enqvist O, Trägårdh E, Saboury B, Lerberg Nielsen A, Loft A, Loaiza Gongora JL, Lopez Urdaneta J, Kumar R, van Essen M, Edenbrandt L. AI Improves agreement and reduces time for quantifying metabolic tumour burden in hodgkin lymphoma. Hematol. Rep. 2025;17:60
• Sadik M, Barrington SF, Trägårdh E, Saboury B, Nielsen AL, Jakobsen AL, Gongora JLL, Urdaneta JL, Kumar R, Edenbrandt L. Metabolic tumour volume in Hodgkin lymphoma-A comparison between manual and AI-based analysis. Clin Physiol Funct Imaging. 2024;44:220.
• Sadik M, López-Urdaneta J, Ulén J, Enqvist O, Andersson PO, Kumar R, Trägårdh E. Artificial Intelligence Increases the Agreement among Physicians Classifying Focal Skeleton/Bone Marrow Uptake in Hodgkin's Lymphoma Patients Staged with 18F-FDG PET/CT-a Retrospective Study. Nucl Med Mol Imaging. 2023;57:110.
• 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.
• Lindgren Belal S, Sadik M, Kaboteh R, Enqvist O, Ulén J, Poulsen MH, Simonsen J, Høilund-Carlsen PF, Edenbrandt L, Trägårdh E. Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases. Eur J Radiol. 2019;113:89.
• Sadik M, Lind E, Polymeri E, Enqvist O, Ulén J, Trägårdh E. 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. Clin Physiol Funct Imaging. 2019;39:78.

Body composition

Publications

• Ying T, Borrelli P, Edenbrandt L, Enqvist O, Kaboteh R, Trägårdh E, Ulén J, Kjölhede H. AI-based fully automatic image analysis: Optimal abdominal and thoracic segmentation volumes for estimating total muscle volume on computed tomography scans. Osteoporos Sarcopenia. 2024;10:78.
• 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.

Automated Bone Scan Index (BSI) – From Research to Global Clinical Impact

Theses

• Kaboteh R. Quantitative analysis of bone scans in prostate cancer patients. Thesis 2013 University of Gothenburg, Sweden.
• Sadik M. Computer-Assisted Diagnosis for the Interpretation of Bone Scintigraphy. A new approach to improve diagnostic accuracy. Thesis 2009 University of Gothenburg, Sweden.

Publications

• Anand A, Heller G, Fox J, Danila DC, Bjartell A, Edenbrandt L, Larson SM, Scher HI, Morris MJ. Automated Bone Scan Index to Optimize Prostate Cancer Working Group Radiographic Progression Criteria for Men With Metastatic Castration-Resistant Prostate Cancer. Clin Genitourin Cancer. 2022;20:270.
• Anand A, Tragardh E, Edenbrandt L, Beckman L, Svensson JH, Thellenberg C, Widmark A, Kindblom J, Ullén A, Bjartell A. Assessing Radiographic Response to Ra-223 with Automated Bone Scan Index in Metastatic Castration Resistant Prostate Cancer Patients. J Nucl Med. 2020;61:671.
• Armstrong AJ, Anand A, Edenbrandt L, Bondesson E, Bjartell A, Widmark A, Sternberg CN, Pili R, Tuvesson H, Nordle Ö, Carducci MA, Morris MJ. Phase 3 Assessment of the Automated Bone Scan Index as a Prognostic Imaging Biomarker of Overall Survival in Men With Metastatic Castration-Resistant Prostate Cancer: A Secondary Analysis of a Randomized Clinical Trial. JAMA Oncol. 2018;4:944.
• Nakajima K, Edenbrandt L, Mizokami A. Bone scan index: A new biomarker of bone metastasis in patients with prostate cancer. Int J Urol. 2017;24:668.
• Anand A, Morris MJ, Kaboteh R, Reza M, Trägårdh E, Matsunaga N, Edenbrandt L, Bjartell A, Larson SM, Minarik D. A Preanalytic Validation Study of Automated Bone Scan Index: Effect on Accuracy and Reproducibility Due to the Procedural Variabilities in Bone Scan Image Acquisition. J Nucl Med. 2016;57:1865.
• Anand A, Morris MJ, Larson SM, Minarik D, Josefsson A, Helgstrand JT, Oturai PS, Edenbrandt L, Røder MA, Bjartell A. Automated Bone Scan Index as a quantitative imaging biomarker in metastatic castration-resistant prostate cancer patients being treated with enzalutamide. EJNMMI Res. 2016;6:23.
• Reza M, Ohlsson M, Kaboteh R, Anand A, Franck-Lissbrant I, Damber JE, Widmark A, Thellenberg-Karlsson C, Budäus L, Steuber T, Eichenauer T, Wollmer P, Edenbrandt L, Trägårdh E, Bjartell A. Bone Scan Index as an Imaging Biomarker in Metastatic Castration-resistant Prostate Cancer: A Multicentre Study Based on Patients Treated with Abiraterone Acetate (Zytiga) in Clinical Practice. European Urology Focus 2016;2:540.
• Reza M, Jones R, Aspegren J, Massard C, Mattila L, Mustonen M, Wollmer P, Trägårdh E, Bondesson E, Edenbrandt L, Fizazi, K, Bjartell A. Bone Scan Index and Progression-free Survival Data for Progressive Metastatic Castration-resistant Prostate Cancer Patients Who Received ODM-201 in the ARADES Multicentre Study. European Urology Focus 2016;2:547.
• Anand A, Morris MJ, Kaboteh R, Båth L, Sadik M, Gjertsson P, Lomsky M, Edenbrandt L, Minarik D, Bjartell A. Analytic Validation of the Automated Bone Scan Index as an Imaging Biomarker to Standardize Quantitative Changes in Bone Scans of Patients with Metastatic Prostate Cancer. J Nucl Med. 2016;57:41.
• Poulsen MH, Rasmussen J, Edenbrandt L, Høilund-Carlsen PF, Gerke O, Johansen A, Lund L. Bone Scan Index predicts outcome in patients with metastatic hormone-sensitive prostate cancer. BJU Int. 2016;117:748.
• Reza M, Bjartell A, Ohlsson M, Kaboteh R, Wollmer P, Edenbrandt L, Trägårdh E. Bone Scan Index as a prognostic imaging biomarker during androgen deprivation therapy. EJNMMI Res. 2014;4:58.
• Armstrong AJ, Kaboteh R, Carducci MA, Damber JE, Stadler WM, Hansen M, Edenbrandt L, Forsberg G, Nordle Ö, Pili R, Morris MJ. Assessment of the bone scan index in a randomized placebo-controlled trial of tasquinimod in men with metastatic castration-resistant prostate cancer (mCRPC). Urol Oncol. 2014;32:1308.
• Kalderstam J, Sadik M, Edenbrandt L, Ohlsson M. Analysis of regional bone scan index measurements for the survival of patients with prostate cancer. BMC Med Imaging. 2014;14:24.
• Tokuda O, Harada Y, Ohishi Y, Matsunaga N, Edenbrandt L. Investigation of computer-aided diagnosis system for bone scans: a retrospective analysis in 406 patients. Ann Nucl Med. 2014;28:329.
• Nakajima K, Nakajima Y, Horikoshi H, Ueno M, Wakabayashi H, Shiga T, Yoshimura M, Ohtake E, Sugawara Y, Matsuyama H, Edenbrandt L. Enhanced diagnostic accuracy for quantitative bone scan using an artificial neural network system: a Japanese multi-center database project. EJNMMI Res. 2013;3:83.
• Kaboteh R, Gjertsson P, Leek H, Lomsky M, Ohlsson M, Sjöstrand K, Edenbrandt L. Progression of bone metastases in patients with prostate cancer - automated detection of new lesions and calculation of bone scan index. EJNMMI Res. 2013;3:64.
• Kaboteh R, Damber JE, Gjertsson P, Höglund P, Lomsky M, Ohlsson M, Edenbrandt L. Bone Scan Index: a prognostic imaging biomarker for high-risk prostate cancer patients receiving primary hormonal therapy. EJNMMI Res. 2013;3:9.
• Kikuchi A, Onoguchi M, Horikoshi H, Sjöstrand K, Edenbrandt L. Automated segmentation of the skeleton in whole-body bone scans: influence of difference in atlas. Nucl Med Commun. 2012;33:947.
• Horikoshi H, Kikuchi A, Onoguchi M, Sjöstrand K, Edenbrandt L. Computer-aided diagnosis system for bone scintigrams from Japanese patients: importance of training database. Ann Nucl Med. 2012;26:622.
• Ulmert D, Kaboteh R, Fox JJ, Savage C, Evans MJ, Lilja H, Abrahamsson PA, Björk T, Gerdtsson A, Bjartell A, Gjertsson P, Höglund P, Lomsky M, Ohlsson M, Richter J, Sadik M, Morris MJ, Scher HI, Sjöstrand K, Yu A, Suurküla M, Edenbrandt L, Larson SM. A novel automated platform for quantifying the extent of skeletal tumour involvement in prostate cancer patients using the Bone Scan Index. Eur Urol. 2012;62:78.
• Levren G, Sadik M, Gjertsson P, Lomsky M, Michanek A, Edenbrandt L. Relation between pain and skeletal metastasis in patients with prostate or breast cancer. Clin Physiol Funct Imaging. 2011;31:193.
• Sjöstrand K, Ohlsson M, Edenbrandt L. Statistical regularization of deformation fields for atlas-based segmentation of bone scintigraphy images. Med Image Comput Assist Interv. 2009;12: 664.
• Sadik M, Suurkula M, Höglund P, Järund A, Edenbrandt L. Improved classifications of planar whole-body bone scans using a computer-assisted diagnosis system: a multicenter, multiple-reader, multiple-case study. J Nucl Med 2009;50:368.
• Sadik M, Hamadeh I, Nordblom P, Suurkula M, Höglund P, Ohlsson M, Edenbrandt L. Computer-Assisted Interpretation of Planar Whole-Body Bone Scans. J Nucl Med 2008;49:1958.
• Sadik M, Suurkula M, Höglund P, Järund A, Edenbrandt L. Quality of planar whole-body bone scan interpretations-a nationwide survey. Eur J Nucl Med Mol Imaging 2008;35:1464.
• Sadik M, Jakobsson D, Olofsson F, Ohlsson M, Suurkula M, Edenbrandt L. A new computer-based decision-support system for the interpretation of bone scans. Nucl Med Commun 2006;27:417.

Cardiac toolbox for Japanese patients

Theses

• Lomsky M.. Automated interpretation of gated-SPECT - A new approach to integrate the analysis of left ventricular perfusion and function. Thesis 2006 University of Gothenburg, Sweden.

Publications

• Nakajima K, Okuda K, Watanabe S, Matsuo S, Kinuya S, Toth K, Edenbrandt L. Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database. Ann Nucl Med. 2018;32:303.
• Nakajima K, Kudo T, Nakata T, Kiso K, Kasai T, Taniguchi Y, Matsuo S, Momose M, Nakagawa M, Sarai M, Hida S, Tanaka H, Yokoyama K, Okuda K, Edenbrandt L. Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study. Eur J Nucl Med Mol Imaging. 2017;44:2280.
• Yoneyama H, Nakajima K, Okuda K, Matsuo S, Onoguchi M, Kinuya S, Edenbrandt L. Reducing the small-heart effect in pediatric gated myocardial perfusion single-photon emission computed tomography. J Nucl Cardiol 2017;24:1378.
• Trägårdh E, Ljungberg M, Edenbrandt L, Örndahl E, Johansson L, Gustafsson A, Jonsson C, Hagerman J, Riklund K, Minarik D. Evaluation of inter-departmental variability of ejection fraction and cardiac volumes in myocardial perfusion scintigraphy using simulated data. EJNMMI Phys 2015;2:2.
• Minarik D, Senneby M, Wollmer P, Mansten A, Sjöstrand K, Edenbrandt L, Trägårdh E. Perfusion vector-a new method to quantify myocardial perfusion scintigraphy images: a simulation study with validation in patients. EJNMMI Res. 2015;5:121.
• Nakajima K, Matsuo S, Wakabayashi H, Yokoyama K, Bunko H, Okuda K, Kinuya S, Nyström K, Edenbrandt L. Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging. Circ J 2015;79:1549.
• Trägårdh E, Hesse B, Knuuti J, Flotats A, Kaufmann PA, Kitsiou A, Hacker M, Verberne HJ, Edenbrandt L, Delgado V, Donal E, Edvardsen T, Galderisi M, Habib G, Lancellotti P, Nieman K, Rosenhek R; EACVI, Agostini D, Gimelli A, Lindner O, Slart R, Ubleis C; EANM. Reporting nuclear cardiology: a joint position paper by the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI). Eur Heart J Cardiovasc Imaging. 2015;16:272.
• Trägårdh E, Carlsson M, Edenbrandt L. Computerized decision making in myocardial perfusion SPECT: The new era in nuclear cardiology? J Nucl Cardiol. 2015;22:885.
• Edenbrandt L, Höglund P, Frantz S, Hasbak P, Johansen A, Johansson L, Kammeier A, Lindner O, Lomsky M, Matsuo S, Nakajima K, Nyström K, Olsson E, Sjöstrand K, Svensson SE, Wakabayashi H, Trägårdh E. Area of ischemia assessed by physicians and software packages from myocardial perfusion scintigrams. BMC Med Imaging 2014;14:5.
• Johansson L, Edenbrandt L, Nakajima K, Lomsky M, Svensson SE, Trägårdh E. Computer-aided diagnosis system outperforms scoring analysis in myocardial perfusion imaging. J Nucl Cardiol 2014;21:416.
• Edenbrandt L, Ohlsson M, Trägårdh E. Prognosis of patients without perfusion defects with and without rest study in myocardial perfusion scintigraphy. EJNMMI Res 2013;3:58.
• Nakajima K, Okuda K, Nyström K, Richter J, Minarik D, Wakabayashi H, Matsuo S, Kinuya S, Edenbrandt L. Improved quantification of small hearts for gated myocardial perfusion imaging. Eur J Nucl Med Mol Imaging 2013;40:1163.
• Trägårdh E, Valind S, Edenbrandt L. Adding attenuation corrected images in myocardial perfusion imaging reduces the need for a rest study. BMC Med Imaging 2013;13:14.
• Johansen A, Lomsky M, Gerke O, Edenbrandt L, Johansson L, Hansen G, Jensen B, Reid MS, Johansson LL, Olofsson C, Minarik D, Nyström K, Wollmer P, Trägårdh E. When is reacquisition necessary due to high extra-cardiac uptake in myocardial perfusion scintigraphy? EJNMMI Res. 2013;3:20.
• Trägårdh E, Sjöstrand K, Edenbrandt L. Normal stress databases in myocardial perfusion scintigraphy--how many subjects do you need? Clin Physiol Funct Imaging. 2012;32:455.
• Trägårdh E, Johansson L, Olofsson C, Valind S, Edenbrandt L. Nuclear medicine technologists are able to accurately determine when a myocardial perfusion rest study is necessary. BMC Med Inform Decis Mak. 2012;12:97.
• Trägårdh E, Höglund P, Ohlsson M, Wieloch M, Edenbrandt L. Referring physicians underestimate the extent of abnormalities in final reports from myocardial perfusion imaging. EJNMMI Res. 2012;2:27.
• Johansson L, Lomsky M, Marving J, Ohlsson M, Svensson S-E, Edenbrandt L. Diagnostic evaluation of three cardiac software packages using a consecutive group of patients. EJNMMI Research 2011;1:2.
• Tragardh E, Sjostrand K, Jakobsson D, Edenbrandt L. Small average differences in attenuation corrected images between men and women in myocardial perfusion scintigraphy: a novel normal stress database. BMC Med Imaging 2011;11:18.
• Gjertsson P, Johansson L, Lomsky M, Ohlsson M, Underwood SR, Edenbrandt L. Clinical data do not improve artificial neural network interpretation of myocardial perfusion scintigraphy. Clin Physiol Funct Imaging 2011;31:240. , 2011.
• Johansson L, Lomsky M, Gjertsson P, Sallerup-Reid M, Johansson J, Ahlin NG, Edenbrandt L. Can Nuclear Medicine Technologists Assess Whether a Myocardial Perfusion Rest Study Is Required? J Nucl Med Technol 2008;36:181.
• Gjertsson P, Ekberg S, Lomsky M, Gan L-M, Edenbrandt L. Evaluation of new automated gated-SPECT and echocardiographic methods for calculating left ventricular volumes and ejection fraction. Int J Cardiol 2009;136:171.
• Tägil K, Marving J, Lomsky M, Hesse B, Edenbrandt L. Use of neural networks to improve quality control of interpretations in myocardial perfusion imaging. Int J Cardiovasc Imaging 2008;24:841.
• Tägil K, Bondouy M, Chaborel JP, Djaballah W, Franken PR, Grandpierre S, Hesse B, Lomsky M, Marie PY, Poisson T, Edenbrandt L. A decision support system improves the interpretation of myocardial perfusion imaging. 2008. Eur J Nucl Med Mol Imaging 2008;35:1602.
• Lomsky M, Gjertsson P, Johansson L, Richter J, Ohlsson M, Tout D, van Aswegen A, Underwood SR, Edenbrandt L. Evaluation of a decision support system for interpretation of myocardial perfusion gated SPECT. Eur J Nucl Med Mol Imaging 2008;35:1523.
• Lomsky M, Johansson L, Gjertsson P, Björk J, Edenbrandt L. Normal limits for left ventricular ejection fraction and volumes determined by gated SPECT – a comparison between two quantification methods. Clin Physiol Funct Imaging 2008;28:169.
• Hesse B, Lindhardt TB, Acampa W, Anagnostopoulos C, Ballinger J, Bax JJ, Edenbrandt L, Flotats A, Germano G, Stopar TG, Franken P, Kelion A, Kjaer A, Le Guludec D, Ljungberg M, Maenhout AF, Marcassa C, Marving J, McKiddie F, Schaefer WM, Stegger L, Underwood R. EANM/ESC guidelines for radionuclide imaging of cardiac function. Eur J Nucl Med Mol Imaging 2008;35:851.
• Gjertsson P, Lomsky M, Richter J, Ohlsson M, Tout D, van Aswegen A, Underwood R, Edenbrandt L. The added value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy and artificial neural networks. Clin Physiol Funct Imaging 2006;5:301.
• Tagil K, Underwood R, Davies G, Latus KA, Ohlsson M, Gotborg CW, Edenbrandt L. Patient gender and radiopharmaceutical tracer is of minor importance for the interpretation of myocardial perfusion images using an artificial neural network. Clin Physiol Funct Imaging. 2006;26:146.
• Lomsky M, Richter J, Johansson L, Hoilund-Carlsen PF, Edenbrandt L. Validation of a new automated method for analysis of gated-SPECT images. Clin Physiol Funct Imaging. 2006;26:139.
• El-Ali HH, Palmer J, Edenbrandt L, Ljungberg M. A model that accounts for the interdependence of extent and severity in the automatic evaluation of myocardial defects. Nucl Med Commun. 2006;27:127.
• El-Ali HH, Palmer J, Carlsson M, Edenbrandt L, Ljungberg M. Interdependence between measures of extent and severity of myocardial perfusion defects provided by automatic quantification programs. Nucl Med Commun 2005;26:1125.
• Anagnostopoulos C, Bardiés M, Bax J, Bengel F, Busemann Sokole E, Cuocolo A, Davies G, Dondi M, Edenbrandt L, Franken F, Hesse B, Kjaer A, Knuuti J, Lassmann M, Ljungberg M, Marcassa C, Marie P Y, McKiddie F, O´Connor M, Prvulovich E, Tägil K, Underwood R, van Eck-Smit B. EANM/ESC procedural guidelines for myocardial perfusion imaging in nuclear cardiology. Eur J Nucl Med Mol Imaging. 2005;32:855.
• Lomsky M, Richter J, Johansson L, El-Ali H, Åström K, Ljungberg M, Edenbrandt L. A new automated method for analysis of gated-SPECT images based on a three-dimensional heart shaped model. Clin Physiol Funct Imaging. 2005;25:234.

PET/CT and CT in prostate cancer

Theses

• Polymeri E. Artificial intelligence-based organ and tumour segmentation in prostate cancer patients. Studies on PET/CT and pre-treatment CT scans. Thesis 2025 University of Gothenburg, Sweden.

Publications

• Trägårdh E, Ulén J, Enqvist O, Larsson M, Valind K, Minarik D, Edenbrandt L. A fully automated AI-based method for tumour detection and quantification on 18F-PSMA-1007 PET-CT images in prostate cancer. EJNMMI Phys. 2025;12:78.
• Trägårdh E, Larsson M, Minarik D, Enqvist O, Edenbrandt L. Evaluation of [18F]PSMA-1007 uptake variability in patients with prostate cancer. Clin Physiol Funct Imaging. 2025;45:e70016.
• Trägårdh E, Ulén J, Enqvist O, Edenbrandt L, Larsson M. Improving sensitivity through data augmentation with synthetic lymph node metastases for AI-based analysis of PSMA PET-CT images. Clin Physiol Funct Imaging. 2024;44:332.
• Polymeri E, Johnsson ÅA, Enqvist O, Ulén J, Pettersson N, Nordström F, Kindblom J, Trägårdh E, Edenbrandt L, Kjölhede H. Artificial intelligence-based organ delineation for radiation treatment planning of prostate cancer on computed tomography. Adv Radiat Oncol. 2023;9:101383.
• Lindgren Belal S, Frantz S, Minarik D, Enqvist O, Wikström E, Edenbrandt L, Trägårdh E. Applications of artificial intelligence in PSMA PET/CT for prostate cancer imaging. Semin Nucl Med. 2024;54:141.
• Lindgren Belal S, Larsson M, Holm J, Buch-Olsen KM, Sörensen J, Bjartell A, Edenbrandt L, Trägårdh E. Automated quantification of PET/CT skeletal tumor burden in prostate cancer using artificial intelligence: The PET index. Eur J Nucl Med Mol Imaging. 2023;50:1510.
• Trägårdh E, Enqvist O, Ulén J, Jögi J, Bitzén U, Hedeer F, Valind K, Garpered S, Hvittfeldt E, Borrelli P, Edenbrandt L. Freely available, fully automated AI-based analysis of primary tumour and metastases of prostate cancer in whole-body 18F-PSMA-1007 PET-CT. Diagnostics (Basel). 2022;12:2101.
• 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;49:3412.
• 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.
• 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.
• 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.
• 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. Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival. Clin Physiol Funct Imaging. 2020;40:106.
• Lindgren Belal S, Sadik M, Kaboteh R, Enqvist O, Ulén J, Poulsen MH, Simonsen J, Høilund-Carlsen PF, Edenbrandt L, Trägårdh E. Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases. Eur J Radiol 2019;113:89.
• Mortensen MA, Borrelli P, Poulsen MH, Gerke O, Enqvist O, Ulén J, Trägårdh E, Constantinescu C, Edenbrandt L, Lund L, Høilund-Carlsen PF. Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study. Clin Physiol Funct Imaging. 2019:39;399.
• 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. 3D skeletal uptake of 18F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer. EJNMMI Res 2017;7:1.