Peer-Reviewed Publication
Eur J Nucl Med Mol Imaging2022;49(3):1041-1051.February 1, 2022Clinical Trial, Phase II
Analytical performance of aPROMISE: automated anatomic contextualization, detection, and quantification of [18F]DCFPyL (PSMA) imaging for standardized reporting.
Kerstin Johnsson1, Johan Brynolfsson1, Hannicka Sahlstedt1, Nicholas G Nickols2,3,4,5, Matthew Rettig4,5,6, Stephan Probst7, Michael J Morris8,9, Anders Bjartell10, Mathias Eiber11, Aseem Anand12,13,14
1Department of Data Science and Machine Learning, EXINI Diagnostics AB, Lund, Sweden.
2Radiation Oncology Service, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
3Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
4Department of Urology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
5Institute of Urologic Oncology, Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA.
6Division of Hematology-Oncology, Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
7Nuclear Medicine, Medical Imaging, Jewish General Hospital, McGill University, Montreal, QC, Canada.
8Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
9Weill Cornell Medical College, New York, NY, USA.
10Department of Translational Medicine, Division of Urological Cancers, Lund University, Lund, Sweden.
11Department of Nuclear Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany.
12Department of Data Science and Machine Learning, EXINI Diagnostics AB, Lund, Sweden. aseem.anand@med.lu.se.
13Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. aseem.anand@med.lu.se.
14Department of Translational Medicine, Division of Urological Cancers, Lund University, Lund, Sweden. aseem.anand@med.lu.se.
Abstract
PURPOSE: The application of automated image analyses could improve and facilitate standardization and consistency of quantification in [18F]DCFPyL (PSMA) PET/CT scans. In the current study, we analytically validated aPROMISE, a software as a medical device that segments organs in low-dose CT images with deep learning, and subsequently detects and quantifies potential pathological lesions in PSMA P…
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