Treatment Response Assessment According to Updated PROMISE Criteria in Patients with Metastatic Prostate Cancer Using an Automated Imaging Platform for Identification, Measurement, and Temporal Tracking of Disease.

Background And Objective: Prostate-specific membrane antigen (PSMA) molecular imaging is widely used for disease assessment in prostate cancer (PC). Artificial intelligence (AI) platforms such as automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) identify and quantify locoregional and distant disease, thereby expediting lesion identification and standardizing reporting. Our aim was to evaluate the ability of the updated aPROMISE platform to assess treatment responses based on integration of the RECIP (Response Evaluation Criteria in PSMA positron emission tomography-computed tomography [PET/CT]) 1.

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

Purpose: The application of automated image analyses could improve and facilitate standardization and consistency of quantification in [F]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.

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

Standardized staging and quantitative reporting are necessary to demonstrate the association of F-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.

Assessment of dosimetric impact of system specific geometric distortion in an MRI only based radiotherapy workflow for prostate.

Dosimetric errors in a magnetic resonance imaging (MRI) only radiotherapy workflow may be caused by system specific geometric distortion from MRI. The aim of this study was to evaluate the impact on planned dose distribution and delineated structures for prostate patients, originating from this distortion. A method was developed, in which computer tomography (CT) images were distorted using the MRI distortion field.