Mami Iima

Preoperative breast magnetic resonance imaging (MRI) is the most sensitive modality for assessing disease extent in newly diagnosed breast cancer, detecting additional ipsilateral disease in approximately 20% of patients. While its impact on patients’ prognosis is debated, MRI may refine surgical management when it is appropriately evaluated. This review provides a practical framework for optimizing MRI interpretation for individualized surgical planning, incorporating the updated American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) v2025 recommendations. Key updates in BI-RADS v2025 include the introduction of “Additional Close Findings” to streamline the management of suspicious lesions within 2.0 cm of the index cancer. For oncoplastic procedures, MRI provides critical metrics: a tumor-nipple distance of > 1.0 cm has been reported as a significant threshold under specific study conditions for predicting nipple involvement in nipple-sparing mastectomy, while an MRI-to-skin distance of 5 mm is a suggested threshold for predicting margin positivity in skin-sparing mastectomy. This review also addresses the assessment of pectoralis muscle involvement and the new BI-RADS section dedicated to lymph node evaluation. Furthermore, MRI is highlighted as an essential tool for identifying eligible candidates for radiofrequency ablation by precisely excluding multifocality and extensive intraductal components to satisfy strict clinical criteria. The integration of these standardized, surgically-relevant metrics allows radiologists to provide a detailed anatomical roadmap, which is essential to maximize cosmetic outcomes through conservative mastectomy techniques without compromising oncological safety.

Intravoxel incoherent motion (IVIM) MRI allows for simultaneous assessment of tissue microcirculation (perfusion) and diffusion of water. In single‐center studies, IVIM has shown great potential for diagnosis, treatment outcome prediction, and treatment monitoring for many different diseases and organs. However, heterogeneity in data acquisition protocols, pre‐processing pipelines, and post‐processing routines yields differences in reported IVIM parameters, which has constrained large‐scale deployment of IVIM. Moreover, deploying IVIM protocols and analysis typically requires technical expertise, further challenging wider use, especially for clinicians. In this consensus paper, to accelerate the deployment of IVIM, we provide recommendations and harmonize protocols for brain, breast, kidney, liver, muscle, and pancreas IVIM studies. For this goal we organized multiple questionnaires and held a dedicated workshop. To ensure a level of standardized, reproducible results, without restricting innovation, we suggest a small subset of b ‐values to always be measured and analyzed separately, and to which more extensive b ‐value sampling can be added for advanced investigations. We further introduce detailed recommendations on acquisition protocols and analysis pipelines. To increase consistency, repeatability, and reproducibility, we highly recommend that these protocols and pipelines be deployed by scientists and clinicians for IVIM studies. For advanced users who desire different protocols or analysis approaches, we suggest adding results from our suggested protocols and analysis pipeline in the supplemental part of their paper to enable retrospective studies.

This correction addresses several errors identified in the original publication

Background: Diffusion‐weighted imaging (DWI) in breast MRI requires balancing image quality with acquisition time. Reducing scan time without sacrificing diagnostic performance could improve patient comfort and workflow. Purpose: To compare 3‐scan trace DWI (tDWI) and diagonal DWI (dDWI) for breast lesion evaluation using both phantom and clinical assessments, focusing on image quality metrics and diagnostic performance. Study Type: Retrospective comparative study. Population: A commercially available breast diffusion phantom (Caliber MRI; Boulder, CO, USA) and 92 consecutive participants were initially enrolled. After excluding 23 due to having no confirmed lesion, and 15 with non‐mass lesions, 54 patients (median age 55.5 years, range 21–82) were analyzed. Field Strength/Sequence: 3‐T, tDWI (120 s) and dDWI (74 s) with identical parameters except for diffusion gradient directions, with dDWI reducing scan time by 38%. Assessment: Phantom studies measured apparent diffusion coefficient (ADC) and estimated signal‐to‐noise ratio (eSNR). Clinical studies evaluated ADC, contrast‐to‐noise ratio (CNR), and contrast ratio (CR) from ROIs in lesions, normal breast tissue, and fat. Three radiologists scored lesion conspicuity and image quality. Statistical Tests: Due to non‐normal data distribution, the Wilcoxon signed‐rank test compared metrics between sequences. The ADC coefficient of variation (CV) was calculated. A p ‐value < 0.05 was considered significant. Results: dDWI significantly improved image quality by reducing artifacts, especially those originating from the nipple and in the breast tissue periphery, and slightly better lesion conspicuity. No significant difference was found for eSNR in breast phantom studies ( p = 0.31 for b = 0; and p = 0.84 for b = 800 s/mm ² ). tDWI demonstrated significantly higher CNR for breast tissue and fat. tDWI also showed lower CR values for tumor/breast tissue and lower values for tumor/fat. ADC measurements were similar between techniques (CV = 4.20%). Data Conclusion: dDWI provides a 38% shorter acquisition time than tDWI while maintaining comparable quantitative performance. dDWI demonstrates improved image quality, particularly in challenging anatomical regions, though tDWI yields higher contrast‐to‐noise ratio values. Evidence Level: 3. Technical Efficacy Stage: 2.

Cervical cancer remains a major cause of mortality among women worldwide, highlighting the need for advances in diagnostic and therapeutic strategies. This review provides a comprehensive overview of recent developments in histopathological classification based on human papillomavirus association, as well as progress in imaging and treatment approaches supported by current research. Emerging technologies, such as artificial intelligence-assisted imaging, targeted molecular therapies, and personalized radiotherapy, hold substantial promise for improving patient outcomes. These innovations offer new possibilities for precision medicine in cervical cancer care, and their current applications are discussed in this review.

This review provides a comprehensive overview of recent transformative advancements in diagnostic imaging that position Japan at the forefront of radiological innovation. We highlight pivotal innovations that enhance diagnostic capabilities and redefine clinical workflows. The article begins with upright multidetector computed tomography (MDCT), a groundbreaking technology offering novel insights into posture-dependent anatomical and physiological changes. We then explore significant progress in breast and gynecologic imaging, including advancements in artificial intelligence computer-aided (AI-CAD) synthesized mammograms, automated breast ultrasound (ABUS), and abbreviated MRI protocols. These innovations address unique regional challenges in early cancer detection. Significant innovations in abdominal radiology, spanning advanced CT (including photon-counting detector CT), accelerated MRI, and AI applications, are also discussed. The review further delves into glymphatic system research, where advanced MRI techniques, particularly DTI-ALPS, are unraveling new insights into brain waste clearance and neurological disorders. Finally, we examine the future of Japanese radiology through the lens of AI, with a focus on Large Language Models (LLMs). We discuss their growing role in diagnostic support, report generation, and information extraction, as well as important societal and ethical considerations. These collective advancements underscore Japan’s dynamic contributions to radiological innovation, poised to significantly impact global healthcare practices by improving disease detection, optimizing workflows, and extending healthy life expectancy in an aging society.