Chronic kidney disease (CKD) is a major global health burden, encompassing a heterogeneous group of disorders with diverse etiologies and pathophysiological mechanisms. Conventional biomarkers such as serum creatinine, estimated glomerular filtration rate (eGFR), and proteinuria are indispensable for detection and staging, yet they may be insensitive to early CKD including diabetic kidney disease (DKD) and can be discordant with underlying structural damage. Over the past 15 years, functional renal MRI has gained attention as a promising noninvasive approach to quantify complementary domains of renal pathology without ionizing radiation or gadolinium-based contrast agents, enabling repeated assessments even in patients with impaired renal function. This review provides a comprehensive overview of multiparametric MRI techniques for evaluating renal microstructure and physiology across CKD, including T1 and T2 mapping for interstitial expansion and inflammatory activity, blood oxygenation level dependent MRI for oxygenation and corticomedullary gradients, diffusion-based methods (apparent diffusion coefficient [ADC], diffusion tensor imaging, intravoxel incoherent motion) for microstructural injury and microvascular components, and arterial spin labeling for perfusion. Corticomedullary differentiation (CMD) is highlighted as an integrative feature across modalities, including signal-intensity-based CMD using noncontrast steady-state free precession (SSFP) with spatially selective inversion recovery and individualized optimal inversion time (multi-TI). Across CKD populations, many MRI-derived metrics correlate with contemporaneous kidney function, and diffusion-derived indices—particularly cortical ADC—have been associated with subsequent eGFR decline, suggesting potential value for progression-risk assessment. DKD is discussed as an important illustrative subtype in which complex pathophysiology may highlight the added value of multiparametric approaches. Clinical translation will depend on standardized acquisition, quality control, and multicenter validation to establish reproducibility and outcome-based thresholds.

Whole-body MRI (WB-MRI) has evolved over the past 2 decades as a noninvasive imaging technique for detecting distant metastases in prostate cancer. Since the introduction of diffusion-weighted imaging with background body signal suppression by Takahara et al. in 2004, its clinical use has expanded rapidly, particularly in the detection of bone metastases. WB-MRI offers whole-body coverage without radiation exposure and can be completed within approximately 30 minutes, making it suitable for repeated examinations. Consequently, it is now applied not only for metastasis detection but also for treatment response evaluation. Diffusion-weighted imaging further enables semi-quantitative assessment of tumor burden by measuring total tumor diffusion volume. Nevertheless, manual processing and interinstitutional standardization remain limitations that hinder widespread clinical adoption. Recent advances in deep learning and quantitative imaging are expected to overcome these issues through automated lesion extraction and volumetric analysis. Moreover, comparative studies have shown that WB-MRI and prostate-specific membrane antigen positron emission tomography can serve as complementary modalities. The integration of both techniques will enhance diagnostic accuracy, facilitate individualized treatment strategies, and contribute to establishing WB-MRI as a next-generation imaging standard in prostate cancer management.

Prostate MRI has become a cornerstone of contemporary prostate cancer diagnosis, enabling improved detection of clinically significant disease while reducing unnecessary biopsies and overtreatment. However, prostate MRI remains technically demanding, time-consuming, and subject to inter-reader variability, particularly as healthcare systems move toward abbreviated protocols such as non-contrast MRI (biparametric MRI). In this context, artificial intelligence (AI) has emerged as a promising tool to enhance image quality, diagnostic consistency, and workflow efficiency across the prostate MRI pathway. This non-systematic narrative review provides a comprehensive overview of the technical foundations, clinical applications, and workflow implications of AI integration into prostate MRI. It summarizes key concepts in machine learning and deep learning relevant to prostate imaging and reviews current evidence supporting AI-based solutions for image quality assessment and reconstruction, automated prostate segmentation, lesion detection, and risk stratification. Particular attention is given to human–AI collaboration models, the role of AI in supporting equivocal lesions, and the integration of imaging with clinical variables for personalized risk estimation. In addition, it discusses the impact of AI on reporting efficiency, training, and standardization, as well as the current landscape of commercially available AI tools. Despite encouraging results from large multicenter studies, important challenges remain, including heterogeneity in study design, limited prospective validation, generalizability across institutions, and ethical and regulatory considerations. Overall, AI should be regarded as a complementary decision-support technology rather than a replacement for radiologists. Thoughtful implementation, robust validation, and appropriate user training are essential to ensure that AI meaningfully enhances the quality, efficiency, and reliability of prostate MRI-based care.

Advances in imaging diagnostics, particularly MRI, have enabled the visualization of clinically significant prostate cancer. Furthermore, advances in image-fusion technology have made it possible to accurately target these visualized lesions and obtain tissue samples through precise needle placement. These developments now enable systematic intraprostatic localization of clinically significant prostate cancer. Traditionally, standard treatment options for localized prostate cancer have involved whole-gland therapies, such as radical prostatectomy and radiation therapy. Although these approaches provide excellent oncological control and favorable long-term outcomes, they are associated with treatment-related adverse events, including urinary incontinence and sexual dysfunction, which can substantially impair quality of life. Focal therapy has emerged as a treatment strategy designed to address these limitations by selectively targeting only the clinically significant cancer within the prostate, thereby aiming to achieve a balance between oncological control and functional preservation. This approach has become technically feasible due to the MRI visibility of clinically significant cancer and precise image-guidance techniques, as well as the development of various ablation technologies. In this review, we provide an overview of MRI/ultrasound fusion–guided biopsy and discuss the current status and clinical implications of focal therapy for localized prostate cancer enabled by this imaging-guided approach.

Bladder cancer carries one of the highest lifetime costs among malignancies, and accurate distinction between non–muscle-invasive and muscle-invasive disease is essential for appropriate treatment selection. Multiparametric MRI (mpMRI) and the Vesical Imaging-Reporting and Data System (VI-RADS) have emerged as key tools for standardizing local staging of bladder cancer; however, their clinical uptake in Japan remains limited. This non-systematic narrative review summarizes the fundamentals and current evidence of VI-RADS, outlines Japan-specific barriers to its implementation, and proposes practical solutions and future perspectives. It describes patient preparation and VI-RADS–compliant mpMRI protocols, sequence-specific criteria for estimating muscle invasion, and the diagnostic performance and reproducibility reported in recent meta-analyses. It also evaluates VI-RADS within the context of major international and Japanese guidelines, highlighting the current gap between imaging-based risk stratification and transurethral resection of bladder tumor (TURBT)–centered decision-making. Particular focus is placed on challenges arising from Japan’s healthcare structure, heterogeneous MRI quality, and shortage of subspecialized radiologists, as well as common diagnostic pitfalls related to technical, reader, and tumor factors. Recent diagnostic advances—including deep learning–based image reconstruction, improved diffusion and dynamic contrast techniques, and qualitative or quantitative adjunct biomarkers, such as peritumoral enhancement, tumor contact length, diffusion kurtosis metrics, radiomics, and artificial intelligence–based prediction models—are reviewed as promising avenues to enhance diagnostic confidence and inter-reader agreement. Finally, the review discusses MRI-first and MRI-guided clinical pathways under investigation, in which VI-RADS–based risk stratification informs the selective use of TURBT, and facilitates more timely, tailored, definitive therapy. In the future, sustained educational efforts, protocol standardization, quality monitoring, and outcome-based prospective trials will be crucial for establishing bladder MRI and VI-RADS as integral components of personalized bladder cancer care in Japan.

Purpose: To compare accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction (DLR-TSE) with conventional T2-weighted TSE (conv-TSE) and accelerated TSE without DLR (non-DLR-TSE), and to evaluate image quality and diagnostic performance of Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1)–based T2 scoring in prostate MRI.

Methods: This single-center retrospective study included 60 patients who underwent prostate MRI with all 3 T2-weighted image sets acquired in the same examination. Qualitative image quality was independently assessed by 2 radiologists using 6 parameters on a 5-point Likert scale. Quantitative metrics included apparent SNR (aSNR), apparent contrast-to-noise ratio (aCNR), and contrast ratio (CR). Diagnostic performance of PI-RADS T2 scores for transition zone lesions was evaluated using receiver operating characteristic (ROC) analysis, sensitivity, specificity, and accuracy. Noninferiority of DLR-TSE relative to conv-TSE was tested with predefined margins. Inter-reader agreement was assessed using weighted kappa statistics.

Results: DLR-TSE demonstrated noninferiority to conv-TSE for all qualitative parameters and quantitative metrics for both readers. Both DLR-TSE and conv-TSE showed significantly higher image quality scores and quantitative values than non-DLR-TSE. For PI-RADS T2 scoring, DLR-TSE achieved diagnostic performance comparable to conv-TSE. For reader 1, the area under the ROC curve (AUC) was identical for DLR-TSE and conv-TSE (0.83; 95%CI 0.69–0.95), and significantly higher than for non-DLR-TSE (0.70; 95%CI 0.56–0.83). Specificity and overall accuracy were markedly reduced with non-DLR-TSE for both readers, whereas sensitivity did not differ significantly among methods. Inter-reader agreement was substantial to almost perfect for DLR-TSE and conv-TSE, and lower for non-DLR-TSE. DLR-TSE reduced acquisition time by approximately 60% compared with conv-TSE.

Conclusion: Accelerated T2-weighted imaging with DLR allows substantial reduction in scan time while maintaining image quality, diagnostic performance, and inter-reader agreement comparable to those of conventional T2-weighted imaging. DLR-TSE may serve as a practical option for improving examination efficiency in clinical prostate MRI.

Purpose: Peritumoral enhancement (PTE) on dynamic contrast-enhanced MRI is a highly specific imaging feature of muscle-invasive bladder cancer (MIBC). However, the histopathological basis of PTE remains unclear. This study aimed to elucidate the pathological substrates underlying PTE, by correlating MRI findings with quantitative histopathological analysis.

Methods: This retrospective cross-sectional study included 14 patients with pathologically confirmed MIBC who underwent preoperative multiparametric MRI followed by radical cystectomy. PTE was assessed on preoperative dynamic contrast-enhanced MRI by 4 experienced radiologists, and its thickness was measured. Postoperatively, histopathological evaluation was performed in 3 regions: intratumoral area (ITA), peritumoral area (PTA), and non-tumoral muscularis propria (MP). Fibrosis was quantified using Masson trichrome staining, T-lymphocytes using CD8 immunohistochemistry, and microvessels using CD31 immunostaining. Quantitative spot-based analysis and continuous ROI-based spatial analysis were performed along the invasive front of the tumor. Regional comparisons were conducted using the Wilcoxon signed-rank test with Bonferroni correction.

Results: PTE thickness on MRI ranged from 1 to 2 mm (median, 1.5 mm), spatially corresponding to the histologically defined PTA. The fibrosis area fraction was significantly higher in the PTA than the ITA and MP (all P < 0.001), with continuous spatial analysis demonstrating a distinct peak immediately outside the tumor invasive margin. T-lymphocyte counts and area fractions were significantly higher in both the ITA and PTA than in the MP, with no significant differences between the ITA and PTA. The number of microvessels was significantly higher in the PTA than in the MP, but did not differ significantly between the PTA and ITA. Microvessel density was significantly higher in the PTA than in both the ITA and MP.

Conclusion: PTE reflects localized stromal remodeling at the tumor invasive front, characterized predominantly by marked peritumoral fibrosis accompanied by increased microvessel density.