Analytical performance of aPROMISE: automated anatomic contextualization, detection, and quantification of [18F]DCFPyL (PSMA) imaging for standardized reporting.

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 PET/CT. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

aPROMISE: A Novel Automated PROMISE Platform to Standardize Evaluation of Tumor Burden in 18F-DCFPyL Images of Veterans with Prostate Cancer.

Standardized staging and quantitative reporting are necessary to demonstrate the association of 18F-DCFPyL PET/CT imaging with clinical outcome. This work introduces an automated platform, aPROMISE, to implement and extend the Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) criteria. The objective is to validate the performance of aPROMISE in staging and quantifying disease burden in patients with prostate cancer who undergo prostate-specific antigen (PSMA) imaging. Methods: This was a retrospective analysis of 109 veterans with intermediate- or high-risk prostate cancer who underwent PSMA imaging. To validate the performance of aPROMISE, 2 independent nuclear medicine physicians conducted aPROMISE-assisted reads, resulting in standardized reports that quantify individual lesions and stage the patients. Patients were staged as having local disease only (miN0M0), regional lymph node disease only (miN1M0), metastatic disease only (miN0M1), or both regional and distant metastatic disease (miN1M1). The staging obtained from aPROMISE-assisted reads was compared with the staging by conventional imaging. Cohen pairwise κ-agreement was used to evaluate interreader variability. Correlation coefficients and intraclass correlation coefficients were used to evaluate the interreader variability of the quantitative assessment (molecular imaging PSMA [miPSMA] index) at each stage. Kendall tau and t testing were used to evaluate the association of miPSMA index with prostate-specific antigen and Gleason score. Results: All PSMA images of 109 veterans met the DICOM conformity and the requirements for the aPROMISE analysis. Both independent aPROMISE-assisted analyses demonstrated significant upstaging in patients with localized (23%, n = 20/87) and regional (25%, n = 2/8) tumor burden. However, a significant number of patients with bone metastases identified on conventional imaging (18F-NaF PET/CT) were downstaged (29%, n = 4/14). The comparison of the 2 independent aPROMISE-assisted reads demonstrated a high κ-agreement: 0.82 for miN0M0, 0.90 for miN1M0, and 0.77 for miN0M1. The Spearman correlation of quantitative miPSMA index was 0.93, 0.96, and 0.97, respectively. As a continuous variable, miPSMA index in the prostate was associated with risk groups defined by prostate-specific antigen and Gleason score. Conclusion: We demonstrated the consistency of the aPROMISE platform between readers and observed substantial upstaging in PSMA imaging compared with conventional imaging. aPROMISE may contribute to broader standardization of PSMA imaging assessment and to its clinical utility in the management of prostate cancer patients.