Analytical performance of aPROMISE: automated anatomic contextualization, detection, and quantification of [18F]DCFPyL (PSMA) imaging for standardized reporting
ConclusionIn 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.
Source: European Journal of Nuclear Medicine and Molecular Imaging - Category: Nuclear Medicine Source Type: research
More News: Cancer | Cancer & Oncology | CT Scan | Learning | Liver | Medical Devices | Nuclear Medicine | PET Scan | Prostate Cancer | Study | Universities & Medical Training | Urology & Nephrology