Introducing aPROMISE

aPROMISE10 is a PACS platform that offers quantitative analysis and standardized reporting of PSMA PET/CT image assessments. Deep Learning technology on PSMA images to enhance:

  • Efficiency: Reduce the laborious task of defining and locating the disease
  • Consistency: Enhance the reproducibility and reliability among the readers
  • Accuracy: Maintain the high diagnostic accuracy
  • Standardization: Enable rapid detection and volumetric quantification of disease burden in PSMA images.


A deep learning-enabled application

  • Automated segmentation and localization of PSMA PET lesion candidates
  • Automated segmentation and quantification of PSMA uptake in reference organs
  • Automated PSMA quantification at lesion level – tumor (miT),  lymph node (miN, miMa), and visceral disease

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Through rigorous performance studies, the aPROMISE has demonstrated:

Reproducible and Quantitative disease-burden Indices

  • Improved inter-reader reproducibility in staging (κ >0.80) and quantification (ICC: 0.99) of prostate cancer patients2,3
  • Quantitative PSMA scan index (PSI) was associated with PSA and Gleason Score2

Accurate Lesion Quantification

  • High segmentation and detection accuracy (>90%) for PSMA lesions in regional and distant lymph Nodes.3

High Efficiency

  • Significant efficiency in creating quantitative structure reporting. Reader spends an average ~3 min minutes for a comprehensive, quantitative report.2

Clinical Utility

  • A quantitative PSMA score/index can help stratify patients for available treatment options2
  • A quantitative PSMA score/index can accurately demonstrate disease progression and response2

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Performance and Safety



  1. Bubendorf, L., et al., Metastatic patterns of prostate cancer: an autopsy study of 1,589 patients. Human pathology, 2000. 31(5): p. 578-83.
  2. Pienta KJ, Gorin MA, Rowe SP, Carroll PR, Pouliot F, Probst S, et al. A Phase 2/3 Prospective Multicenter Study of the Diagnostic Accuracy of Prostate Specific Membrane Antigen PET/CT with (18)F-DCFPyL in Prostate Cancer Patients (OSPREY). The Journal of urology. 2021:101097JU0000000000001698.
  3. Morris MJ, Rowe SP, Gorin MA, Saperstein L, Pouliot F, Josephson D, et al. Diagnostic Performance of (18)F- DCFPyL-PET/CT in Men with Biochemically Recurrent Prostate Cancer: Results from the CONDOR Phase III, Multicenter Study. Clinical cancer research : an official journal of the American Association for Cancer Research. 2021.
  4. Eiber M, Herrmann K, Calais J, Hadaschik B, Giesel FL, Hartenbach M, et al. Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE): Proposed miTNM Classification for the Interpretation of PSMA-Ligand PET/CT. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2018;59(3):469-78.
  5. Fanti S, Minozzi S, Morigi JJ, Giesel F, Ceci F, Uprimny C, et al. Development of standardized image interpretation for 68Ga-PSMA PET/CT to detect prostate cancer recurrent lesions. European journal of nuclear medicine and molecular imaging. 2017;44(10):1622-35.
  6. Rowe SP, Pienta KJ, Pomper MG, Gorin MA. PSMA-RADS Version 1.0: A Step Towards Standardizing the Interpretation and Reporting of PSMA-targeted PET Imaging Studies. European urology. 2018;73(4):485-7.
  7. Ceci F, Oprea-Lager DE, Emmett L, Adam JA, Bomanji J, Czernin J, et al. E-PSMA: the EANM standardized reporting guidelines v1.0 for PSMA-PET. European journal of nuclear medicine and molecular imaging. 2021;48(5):1626-38.
  8. Nickols N, Anand A, Johnsson K, et al. aPROMISE: A Novel Automated-PROMISE platform to Standardize Evaluation of Tumor Burden in (18)F-DCFPyL (PSMA) images of Veterans with Prostate Cancer. J Nucl Med. 2021.
  9. Johnsson K, Brynolfsson J, Sahlstedt H, et al. Analytical performance of aPROMISE: automated anatomic contextualization, detection, and quantification of [(18)F]DCFPyL (PSMA) imaging for standardized reporting. Eur J Nucl Med Mol Imaging. 2021.
  10. A unique configuration is also available in EU under the name PYLCLARI AI