Retrospective analysis of multiparametric MRI in predicting complete pathologic response of neo-adjuvant chemotherapy in bladder cancer.

Background: Muscle invasive bladder cancer (MIBC) treatment combines systemic therapy and radical cystectomy (RC) or local (chemo-)radiotherapy. Response to systemic therapy is an important outcome predictor but is difficult to assess pre-operatively.

Methods: We analyzed multiparametric MRI (mpMRI) in consecutive MIBC patients receiving cisplatin-based neo-adjuvant chemotherapy at our institution.

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study.

Background: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.

PI-QUAL version 2: an update of a standardised scoring system for the assessment of image quality of prostate MRI.

Multiparametric MRI is the optimal primary investigation when prostate cancer is suspected, and its ability to rule in and rule out clinically significant disease relies on high-quality anatomical and functional images. Avenues for achieving consistent high-quality acquisitions include meticulous patient preparation, scanner setup, optimised pulse sequences, personnel training, and artificial intelligence systems. The impact of these interventions on the final images needs to be quantified.