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 PET/CT.
To evaluate the deep learning algorithm, the automated segmentations of the low-dose CT component of PSMA PET/CT scans from 20 patients were compared to manual segmentations. Dice scores were used to quantify the similarities between the automated and manual segmentations. Next, the automated quantification of tracer uptake in the reference organs and detection and pre-segmentation of potential lesions were evaluated in 339 patients with prostate cancer, who were all enrolled in the phase II/III OSPREY study. Three nuclear medicine physicians performed the retrospective independent reads of OSPREY images with aPROMISE. Quantitative consistency was assessed by the pairwise Pearson correlations and standard deviation between the readers and aPROMISE. The sensitivity of detection and pre-segmentation of potential lesions was evaluated by determining the percent of manually selected abnormal lesions that were automatically detected by aPROMISE.
The Dice scores for bone segmentations ranged from 0.88 to 0.95. The Dice scores of the PSMA PET/CT reference organs, thoracic aorta and liver, were 0.89 and 0.97, respectively. Dice scores of other visceral organs, including prostate, were observed to be above 0.79. The Pearson correlation for blood pool reference was higher between any manual reader and aPROMISE, than between any pair of manual readers. The standard deviations of reference organ uptake across all patients as determined by aPROMISE (SD = 0.21 blood pool and SD = 1.16 liver) were lower compared to those of the manual readers. Finally, the sensitivity of aPROMISE detection and pre-segmentation was 91.5% for regional lymph nodes, 90.6% for all lymph nodes, and 86.7% for bone in metastatic patients.
In this analytical study, we demonstrated the segmentation accuracy of the deep learning algorithm, the consistency in quantitative assessment across multiple readers, and the high sensitivity in detecting potential lesions. The study provides a foundational framework for clinical evaluation of aPROMISE in standardized reporting of PSMA PET/CT.
European journal of nuclear medicine and molecular imaging. 2021 Aug 31 [Epub ahead of print]
Kerstin Johnsson, Johan Brynolfsson, Hannicka Sahlstedt, Nicholas G Nickols, Matthew Rettig, Stephan Probst, Michael J Morris, Anders Bjartell, Mathias Eiber, Aseem Anand
Department of Data Science and Machine Learning, EXINI Diagnostics AB, Lund, Sweden., Radiation Oncology Service, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA., Department of Urology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA., Nuclear Medicine, Medical Imaging, Jewish General Hospital, McGill University, Montreal, QC, Canada., Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Department of Translational Medicine, Division of Urological Cancers, Lund University, Lund, Sweden., Department of Nuclear Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany., Department of Data Science and Machine Learning, EXINI Diagnostics AB, Lund, Sweden. .