Publications by authors named "D Yakar"
Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI scans of 1143 male patients (median age, 64 years; IQR, 59-68 years) undergoing MRI for suspicion of clinically significant PCa (csPCa) (International Society of Urological Pathology grade > 1) between January 2012 and May 2022 who were negative for csPCa at baseline MRI. A DL model was developed using baseline MRI and clinical parameters (age, prostate-specific antigen [PSA] level, PSA density, and prostate volume) to predict the time to PCa progression (defined as csPCa diagnosis at follow-up).
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- Biparametric MRI (bpMRI) may serve as a valid alternative to multiparametric MRI (mpMRI) for diagnosing clinically significant prostate cancer (csPCa), as assessed in a large international observer study.
- The study involved 400 mpMRI examinations from four different European centers, where readers evaluated both bpMRI and mpMRI for their ability to accurately diagnose csPCa, finding them to be similarly effective.
- Key findings indicated that bpMRI and mpMRI had comparable diagnostic accuracy (AUROC values) and sensitivity, with bpMRI showing a noninferior performance, though both methods had similar specificity when distinguishing csPCa.
While knowledge of the population's view on the need for informed consent for retrospective radiology research may provide valuable insight into how an optimal balance can be achieved between patient rights versus an expedited advancement of radiology science, this is a topic that has been ignored in the literature so far. To investigate the view of the general population, survey data were collected from 2407 people representative of the Dutch population. The results indicate that for non-commercial institutions, especially hospitals (97.
View Article and Find Full Text PDFObjectives: 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.