Publications by authors named "D Rouw"
Objectives: This study investigated patients' acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses.
Materials And Methods: A prospective, multicenter study was conducted between January and November 2023. Patients undergoing prostate MRI were surveyed about their opinions on hypothetical AI assessment of their MRI scans.
Article Synopsis
- This study investigates the potential for improving the diagnostic accuracy of detecting clinically significant prostate cancer (csPCa) on MRI by incorporating clinical parameters like prostate-specific antigen, prostate volume, and age into deep learning models.
- A total of 932 biparametric MRI exams were analyzed, and various AI models were tested, combining MRI-based deep learning results with the clinical parameters through different methods of data fusion.
- The results showed that the best model, which combined deep learning suspicion levels with clinical features, outperformed other models and had performance comparable to radiologist assessments in identifying csPCa.
Aim: To evaluate the albuminuria-lowering effect of dapagliflozin, exenatide, and the combination of dapagliflozin and exenatide in patients with type 2 diabetes and microalbuminuria or macroalbuminuria.
Methods: Participants with type 2 diabetes, an estimated glomerular filtration rate (eGFR) of more than 30 ml/min/1.73m and an urinary albumin: creatinine ratio (UACR) of more than 3.
Objectives: To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI).
Materials And Methods: This study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented with a DLM auto-fixed VOI method (averaging < 10 s per lesion) and manually segmented by an expert uroradiologist (averaging 5 min per lesion).
Objectives: To investigate a previously developed radiomics-based biparametric magnetic resonance imaging (bpMRI) approach for discrimination of clinically significant peripheral zone prostate cancer (PZ csPCa) using multi-center, multi-vendor (McMv) and single-center, single-vendor (ScSv) datasets.
Methods: This study's starting point was a previously developed ScSv algorithm for PZ csPCa whose performance was demonstrated in a single-center dataset. A McMv dataset was collected, and 262 PZ PCa lesions (9 centers, 2 vendors) were selected to identically develop a multi-center algorithm.