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.

Validation of 3D finite element models from simulated DXA images for biofidelic simulations of sideways fall impact to the hip.

Computed tomography (CT)-derived finite element (FE) models have been proposed as a tool to improve the current clinical assessment of osteoporosis and personalized hip fracture risk by providing an accurate estimate of femoral strength. However, this solution has two main drawbacks, namely: (i) 3D CT images are needed, whereas 2D dual-energy x-ray absorptiometry (DXA) images are more generally available, and (ii) quasi-static femoral strength is predicted as a surrogate for fracture risk, instead of predicting whether a fall would result in a fracture or not. The aim of this study was to combine a biofidelic fall simulation technique, based on 3D computed tomography (CT) data with an algorithm that reconstructs 3D femoral shape and BMD distribution from a 2D DXA image.