(1) Background: Recent studies report high accuracies when using machine learning (ML) algorithms to classify prostate cancer lesions on publicly available datasets. However, it is unknown if these trained models generalize well to data from different institutions. (2) Methods: This was a retrospective study using multi-parametric Magnetic Resonance Imaging (mpMRI) data from our institution (63 mpMRI lesions) and the ProstateX-2 challenge, a publicly available annotated image set (112 mpMRI lesions). Residual Neural Network (ResNet) algorithms were trained to classify lesions as high-risk (hrPCA) or low-risk/benign. Models were trained on (a) ProstateX-2 data, (b) local institutional data, and (c) combined ProstateX-2 and local data. The models were then tested on (a) ProstateX-2, (b) local and (c) combined ProstateX-2 and local data. (3) Results: Models trained on either local or ProstateX-2 image data had high Area Under the ROC Curve (AUC)s (0.82–0.98) in the classification of hrPCA when tested on their own respective populations. AUCs decreased significantly (0.23–0.50, p < 0.01) when models were tested on image data from the other institution. Models trained on image data from both institutions re-achieved high AUCs (0.83–0.99). (4) Conclusions: Accurate prostate cancer classification models trained on single-institutional image data performed poorly when tested on outside-institutional image data. Heterogeneous multi-institutional training image data will likely be required to achieve broadly applicable mpMRI models.

Renal hypoxia has recently been implicated as a key contributor and indicator of various glomerular diseases. As such, monitoring changes in renal oxygenation in these disorders may provide an early diagnostic advantage that could prevent potential adverse outcomes. Blood oxygen level-dependent magnetic resonance imaging (BOLD MRI) is an emerging noninvasive technique for assessing renal oxygenation in glomerular disease. Although BOLD MRI has produced promising initial results for the use in certain renal pathologies, the use of BOLD imaging in glomerular diseases, including primary and secondary nephrotic and nephritic syndromes, is relatively unexplored. Early BOLD studies on primary nephrotic syndrome, nephrotic syndrome secondary to diabetes mellitus, and nephritic syndrome secondary to systemic lupus erythematosus have shown promising results to support its future clinical utility. In this review, we outline the advancements made in understanding the use of BOLD MRI for the assessment, diagnosis, and screening of these pathologies.