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

ABSTRACT:

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 detect

SUBMITTER: Johnsson K 

PROVIDER: S-EPMC8803714 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature