aPROMISE Platform
aPROMISE provides accurate quantitative imaging biomarkers of clinical value, increasing reproducibility in the assessment of disease. aPROMISE enables a standardized, streamlined approach to determine patient eligibility for treatment with radioligand therapy, offering automated and accurate calculation of whole body SUVmean.
AI-Based Detection and Adaptive Segmentation of Hotspots:
- Deep-learning technology, aPROMISE produces total-body PSMA detection and adaptive segmentation of lesions with a single click.
- Produces total-body quantification in both auto-segmented and manually selected lesions.
- Automated PSMA quantification at lesion level, including anatomical location and tissue type - tumor (miT), lymph node (miN, miMa), and visceral disease.
Total-body disease quantification is automated, allowing for considerable time-savings compared to manual calculation, particularly in patients with high-burden metastatic disease.
References:
- FDA clearance letter for aPROMISE X. Food and Drug Administration. April 29, 2022. (Read article)
- Nickols N, Anand A, Johnsson K, et al. aPROMISE: A Novel Automated PROMISE Platform to Standardize Evaluation of Tumor Burden in18F-DCFPyL Images of Veterans with Prostate Cancer. J Nucl Med. 2022;63(2):233-239. doi:10.2967/jnumed.120.261863. (Read article)
- Johnsson K, Brynolfsson J, Sahlstedt H, et al. Analytical performance of aPROMISE: automated anatomic contextualization, detection, and quantification of [18F]DCFPyL (PSMA) imaging for standardized reporting. Eur J Nucl Med Mol Imaging. 2022;49(3):1041-1051. doi:10.1007/s00259-021-05497-8. (Read article)
Updated Automated PROMISE Assessment
Treatment Response Evaluation Approach on Metastatic Prostate Cancer Patients Based on PSMA PET/CT
All 33 cases were automatically given the same class as they had been given manually.
Conclusions:
- The AI enabled automated platform can efficiently, consistently, and accurately implement PSMA consensus guidelines.
- Implementation of the guidelines in clinical routine setting is essential for the clinical utility of PSMA PET-CT.
Case Examples of aRECIP
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