Publishing type：Research paper (scientific journal)  

DOI： 10.3390/tomography12010006

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Magnetic Resonance in Medicine  

DOI： 10.2463/mrms.rev.2025-0150

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The emergence of Artificial Superintelligence (ASI) in healthcare presents unprecedented opportunities for revolutionizing diagnostics, treatment planning, and population health management, but also introduces critical risks if these systems are not properly aligned with human values and clinical objectives. This review examines the theoretical foundations of ASI and the alignment problem in healthcare contexts, exploring how misaligned Artificial Intelligence (AI) systems could optimize for wrong objectives or pursue harmful strategies leading to patient harm and systemic failures. Current challenges in AI alignment are illustrated through real-world examples from radiology and clinical decision-making, where algorithms have demonstrated concerning biases, generalizability failures, and optimization for inappropriate proxy measures. The paper analyzes key alignment challenges including objective complexity and technical pitfalls, bias and fairness issues in healthcare data, ethical integration concerns involving compassion and patient autonomy, and system-level policy challenges around regulation and liability. Technical alignment strategies are discussed including reinforcement learning from human feedback, interpretability requirements, formal verification methods, and adversarial testing approaches. Normative alignment solutions encompass ethical frameworks, professional standards, patient engagement protocols, and multi-level governance structures spanning institutional, national, and international coordination. The review emphasizes that successful ASI alignment in healthcare requires combining cutting-edge AI research with fundamental medical ethics, noting that while proper alignment could enable transformative health improvements and medical breakthroughs, misalignment risks undermining the core purpose of medicine. The stakes of this alignment challenge are characterized as among the highest in both technology and ethics, with implications extending from individual patient safety to public trust and potentially existential risks.

DOI： 10.1007/s11604-025-01907-1

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Language：English   Publisher：Radiologia Medica  

Recent advances in molecular genetics have revolutionized the classification of pediatric-type high-grade gliomas in the 2021 World Health Organization central nervous system tumor classification. This narrative review synthesizes current evidence on the following four tumor types: diffuse midline glioma, H3 K27-altered; diffuse hemispheric glioma, H3 G34-mutant; diffuse pediatric-type high-grade glioma, H3-wildtype and IDH-wildtype; and infant-type hemispheric glioma. We conducted a comprehensive literature search for articles published through January 2025. For each tumor type, we analyze characteristic clinical presentations, molecular alterations, conventional and advanced magnetic resonance imaging features, radiological-molecular correlations, and current therapeutic approaches. Emerging radiogenomic approaches utilizing artificial intelligence, including radiomics and deep learning, show promise in identifying imaging biomarkers that correlate with molecular features. This review highlights the importance of integrating radiological and molecular data for accurate diagnosis and treatment planning, while acknowledging limitations in current methodologies and the need for prospective validation in larger cohorts. Understanding these correlations is crucial for advancing personalized treatment strategies for these challenging tumors.

DOI： 10.1007/s11547-025-02078-9

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Breast magnetic resonance imaging (MRI) is an essential tool for evaluating breast lesions, with dynamic contrast-enhanced (DCE) MRI being considered the reference standard. However, conventional DCE-MRI has limitations, including long scan times, high costs, and variable specificity leading to unnecessary biopsies. Emerging techniques such as ultrafast dynamic contrast-enhanced (UF-DCE) MRI and diffusion-weighted imaging (DWI) have recently received attention as possible alternatives. UF-DCE MRI achieves high temporal resolution, improving lesion conspicuity while reducing motion artifacts and background parenchymal enhancement. Advanced acceleration methods, including view sharing and compressed sensing, enhance temporal resolution while maintaining image quality. DWI, a contrast agent-free technique that can be used to assess tissue cellularity, provides high specificity in the differentiation of benign from malignant lesions. Recent developments in DWI, such as readout-segmented echo planar imaging, reduced field of view, and simultaneous multi-slice techniques, have significantly improved spatial resolution and reduced artifacts. These advancements enable morphological assessment and hold the potential for replacing or complementing conventional DCE-MRI, thus reducing patient burden and improving accessibility. Future research should focus on optimizing imaging protocols and integrating artificial intelligence to enhance diagnostic performance. This review discusses the principles, technological advancements, and clinical applications of UF-DCE MRI and DWI, with a particular focus on morphological evaluation and image quality, emphasizing their role in improving the efficiency of breast imaging while maintaining accuracy.

DOI： 10.1007/s11604-025-01826-1

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1177/08465371251374557

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Language：English   Publishing type：Research paper (scientific journal)  

This review provides a comprehensive overview of current practices and recent advancements in the diagnosis and treatment of renal cancer. It introduces updates in histological classification and explains the imaging characteristics of each tumour based on these changes. The review highlights state-of-the-art imaging modalities, including magnetic resonance imaging, computed tomography, positron emission tomography, and ultrasound, emphasising their crucial role in tumour characterisation and optimising treatment planning. Emerging technologies, such as radiomics and artificial intelligence, are also discussed for their transformative impact on enhancing diagnostic precision, prognostic prediction, and personalised patient management. Furthermore, the review explores current treatment options, including minimally invasive techniques such as cryoablation, radiofrequency ablation, and stereotactic body radiation therapy, as well as systemic therapies such as immune checkpoint inhibitors and targeted therapies.

DOI： 10.1007/s11547-025-02066-z

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Photon-counting detector computed tomography (PCD-CT) is an emerging imaging technology that promises to overcome the limitations of conventional energy-integrating detector (EID)-CT, particularly in thoracic oncology. This narrative review summarizes technical advances and clinical applications of PCD-CT in the thorax with emphasis on spatial resolution, dose-image-quality balance, and intrinsic spectral imaging, and it outlines practical implications relevant to thoracic oncology. A literature review of PubMed through May 31, 2025, was conducted using combinations of "photon counting," "computed tomography," "thoracic oncology," and "artificial intelligence." We screened the retrieved records and included studies with direct relevance to lung and mediastinal tumors, image quality, radiation dose, spectral/iodine imaging, or artificial intelligence-based reconstruction; case reports, editorials, and animal-only or purely methodological reports were excluded. PCD-CT demonstrated superior spatial resolution compared with EID-CT, enabling clearer visualization of fine pulmonary structures, such as bronchioles and subsolid nodules; slice thicknesses of approximately 0.4 mm and ex vivo resolvable structures approaching 0.11 mm have been reported. Across intraindividual clinical comparisons, radiation-dose reductions of 16%-43% have been achieved while maintaining or improving diagnostic image quality. Intrinsic spectral imaging enables accurate iodine mapping and low-keV virtual monoenergetic images and has shown quantitative advantages versus dual-energy CT in phantoms and early clinical work. Artificial intelligence-based deep-learning reconstruction and super-resolution can complement detector capabilities to reduce noise and stabilize fine-structure depiction without increasing dose. Potential reductions in contrast volume are biologically plausible given improved low-keV contrast-to-noise ratio, although clinical dose-finding data remain limited, and routine K-edge imaging has not yet translated to clinical thoracic practice. In conclusion, PCD-CT provides higher spatial and spectral fidelity at lower or comparable doses, supporting earlier and more precise tumor detection and characterization; future work should prioritize outcome-oriented trials, protocol harmonization, and implementation studies aligned with "Green Radiology".

DOI： 10.4274/dir.2025.253550

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

The fifth edition of the World Health Organization classification of central nervous system tumors represents a significant advancement in the molecular-genetic classification of pediatric-type diffuse gliomas. This article comprehensively summarizes the clinical, molecular, and radiological imaging features in pediatric-type low-grade gliomas (pLGGs), including MYB- or MYBL1-altered tumors, polymorphous low-grade neuroepithelial tumor of the young (PLNTY), and diffuse low-grade glioma, MAPK pathway-altered. Most pLGGs harbor alterations in the RAS/MAPK pathway, functioning as "one pathway disease". Specific magnetic resonance imaging features, such as the T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign in MYB- or MYBL1-altered tumors and the transmantle-like sign in PLNTYs, may serve as non-invasive biomarkers for underlying molecular alterations. Recent advances in radiogenomics have enabled the differentiation of BRAF fusion from BRAF V600E mutant tumors based on magnetic resonance imaging characteristics. Machine learning approaches have further enhanced our ability to predict molecular subtypes from imaging features. These radiology-molecular correlations offer potential clinical utility in treatment planning and prognostication, especially as targeted therapies against the MAPK pathway emerge. Continued research is needed to refine our understanding of genotype-phenotype correlations in less common molecular alterations and to validate these imaging biomarkers in larger cohorts.

DOI： 10.1007/s11547-025-02049-0

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Language：English   Publishing type：Research paper (scientific journal)  

Theranostics, a novel integrated approach that combines cancer diagnosis and therapy by switching the radionuclide, has attracted growing attention. Various oncology PET probes other than FDG have been developed for the highly sensitive and precise detection of many types of cancer with the advancements in PET scanners, supporting the innovative development in theranostics. In therapeutic applications, radioligand therapy targeting somatostatin receptors (SSTR) and prostate-specific membrane antigen (PSMA) has already demonstrated significant clinical benefits. Terbium-161 (161Tb) has emerged as a new beta and Auger electron emitter, showing greater therapeutic efficacy compared to 177Lu. Alpha emitters, such as astatine (211At), are currently being evaluated in investigator-initiated clinical trials, with preliminary efficacy data reported for [211At]NaAt in patients with radioiodine-refractory thyroid cancer. Novel pan-tumor targeting agents, such as TROP-2, Nectin-4, LAT1, GPC-1, and EphA2, are also under development, and clinical translation of radioligand therapy is anticipated. These innovations in theranostics are expected to further broaden the scope of precision medicine in oncology.

DOI： 10.1007/s12149-025-02090-z

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DOI： 10.1007/s11604-025-01803-8

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Language：English   Publishing type：Research paper (scientific journal)  

PURPOSE: We aimed to evaluate whether the ALPS index derived from diffusion tensor image analysis along the perivascular space (DTI-ALPS) is influenced by white matter fibres within the analysis region, particularly commissural fibres from the corpus callosum that traverse this area in psychiatric patients with suspected obstructive sleep apnea (OSA). We also investigated associations between diffusion-based parameters, sleep-related data, and neurofluid-related imaging metrics. METHODS: Fifty participants with OSA underwent brain magnetic resonance imaging and polysomnography, including diffusion tensor and structural sequences. Among them, 8 participants had no psychiatric comorbidities, while the remaining 42 had various psychiatric disorders in addition to OSA. Diffusion-based parameters were obtained, and both the original and variant ALPS index were calculated. Correlation analyses were conducted with sleep-related data and neurofluid-related imaging parameters, including choroid plexus volume (CPV) and volume of white matter lesion burden (WMHV). Mediation analyses were also performed to explore the influence of white matter diffusivity on the perivascular diffusivity index. RESULTS: The ALPS index showed weak to moderate correlations with multiple sleep-related variables. It also correlated with CPV and WMHV. Mediation analyses demonstrated that diffusivity within white matter fibres was associated with the ALPS index. Moreover, variant ALPS indices measured in the corpus callosum may reflect fluid motion in the direction of perivascular spaces. CONCLUSION: These findings suggest that the ALPS index is influenced by both diffusivity along perivascular spaces and white matter microstructure, particularly commissural fibres. Although it should not be regarded as a highly specific marker of perivascular space function, variant indices support partial perivascular contribution. Furthermore, associations with sleep and neurofluid-related metrics imply that white matter architecture and inter-fibre spaces may serve as plausible routes for interstitial fluid flow.

DOI： 10.1007/s11604-025-01838-x

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Language：English   Publishing type：Research paper (scientific journal)  

Purpose To evaluate the performance of a machine learning model developed using radiomics data derived from physiologically decomposed diffusion-weighted MRI data for predicting pathologic complete response (pCR) following neoadjuvant chemotherapy for breast cancer compared with baseline and benchmark models. Materials and Methods This retrospective study included data from the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge dataset, comprising longitudinal multiparametric breast MRI studies (diffusion-weighted imaging [DWI] and dynamic contrast-enhanced MRI) from participants enrolled in the I-SPY 2/ACRIN 6698 trial (ClinicalTrials.gov: NCT01042379). Piecewise linear physiologic decomposition was applied to DWI data (PD DWI) to isolate pseudo-diffusion, pure-diffusion, and pseudo-diffusion fraction components for radiomics feature extraction. These features were used to develop a boosted decision tree model to predict pCR following neoadjuvant chemotherapy. Model performance was compared with performance of baseline models, including data on tumor size and mean apparent diffusion coefficient, and the BMMR2 challenge benchmark model using area under the receiver operating characteristic curve, F1 score, and positive and negative predictive values. Model calibration was assessed via the Brier score, and a decision curve analysis was performed to estimate the potential reduction in unnecessary interventions when using the proposed model. Results The study included multiparametric MRI scans from 190 female participants (mean age ± SD, 48.4 years ± 10.5). PD DWI achieved the highest area under the receiver operating characteristic curve (0.89, 95% CI: 0.81, 0.96) among all evaluated models, demonstrating statistically significant improvements over baseline approaches (all P < .04). Decision curve analysis showed that the PD DWI model provided a greater net benefit compared with the BMMR2 challenge benchmark model (0.17, 95% CI: 0.13, 0.21 vs 0.09, 95% CI: 0.05, 0.13; P < .001). Conclusion A machine learning model using radiomics data derived from PD DWI achieved higher performance than baseline and benchmark models in predicting pCR following neoadjuvant chemotherapy for breast cancer. Keywords: Image Postprocessing, MR-Diffusion Weighted Imaging, Breast, Tumor Response, Experimental Investigations ClinicalTrials.gov: NCT01042379 © RSNA, 2025.

DOI： 10.1148/rycan.240312

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Language：English   Publishing type：Research paper (scientific journal)  

The brain and spinal cord, which constitute the central nervous system, were historically considered immune-privileged sites, as it was believed they lacked an equivalent to the systemic lymphatic system. However, in 2013, a pathway facilitating the clearance of waste products through the brain parenchyma via the perivascular space was proposed, garnering attention as the 'glymphatic system'. Similar to the systemic lymphatic system, the glymphatic system plays a critical role in immune responses and has been implicated not only in Alzheimer's disease and inflammatory brain disorders but also in conditions such as hydrocephalus and glaucoma, which are associated with cerebrospinal fluid circulation impairments. Recent studies have suggested that dysfunction of the glymphatic system may promote the progression of brain tumors and reduce the efficacy of immune responses and pharmacological therapies targeting tumors. Radiotherapy is a major treatment option for brain tumors; however, while it can enhance immune responses against tumors, it may also suppress these responses at the same time. Additionally, cranial irradiation has been suggested to impair the function of the glymphatic system. This review provides an overview of the structure and functional evaluation methods of the glymphatic system, summarizes the effects of its dysfunction on brain tumor treatment, and explores recent findings on the impact of radiation therapy on glymphatic system functioning. Lastly, it also explores the potential for radiation therapy strategies that account for their effects on the glymphatic system.

DOI： 10.1093/jrr/rraf027

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DOI： 10.1148/radiol.250964

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DOI： 10.1148/rycan.250129

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OBJECTIVES: This study by the EUSOBI International Breast Diffusion-weighted Imaging (DWI) working group aimed to evaluate the current and future applications of advanced DWI in breast imaging. METHODS: A literature search and a comprehensive survey of EUSOBI members to explore the clinical use and potential of advanced DWI techniques and a literature search were involved. Advanced DWI approaches such as intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion tensor imaging (DTI) were assessed for their current status and challenges in clinical implementation. RESULTS: Although a literature search revealed an increasing number of publications and growing academic interest in advanced DWI, the survey revealed limited adoption of advanced DWI techniques among EUSOBI members, with 32% using IVIM models, 17% using non-Gaussian diffusion techniques for kurtosis analysis, and only 8% using DTI. A variety of DWI techniques are used, with IVIM being the most popular, but less than half use it, suggesting that the study identified a gap between the potential benefits of advanced DWI and its actual use in clinical practice. CONCLUSION: The findings highlight the need for further research, standardization and simplification to transition advanced DWI from a research tool to regular practice in breast imaging. The study concludes with guidelines and recommendations for future research directions and clinical implementation, emphasizing the importance of interdisciplinary collaboration in this field to improve breast cancer diagnosis and treatment. CLINICAL RELEVANCE STATEMENT: Advanced DWI in breast imaging, while currently in limited clinical use, offers promising improvements in diagnosis, staging, and treatment monitoring, highlighting the need for standardized protocols, accessible software, and collaborative approaches to promote its broader integration into routine clinical practice. KEY POINTS: Increasing number of publications on advanced DWI over the last decade indicates growing research interest. EUSOBI survey shows that advanced DWI is used primarily in research, not extensively in clinical practice. More research and standardization are needed to integrate advanced DWI into routine breast imaging practice.

DOI： 10.1007/s00330-024-11010-0

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Language：English   Publishing type：Research paper (scientific journal)  

Recent advancements in breast magnetic resonance imaging (MRI) have significantly enhanced breast cancer detection and characterization. Breast MRI offers superior sensitivity, particularly valuable for high-risk screening and assessing disease extent. Abbreviated protocols have emerged, providing efficient cancer detection while reducing scan time and cost. Diffusion-weighted imaging (DWI), a non-contrast technique, has shown promise in differentiating malignant from benign lesions. It offers shorter scanning times and eliminates contrast agent risks. Apparent diffusion coefficient (ADC) values provide quantitative measures for lesion characterization, potentially reducing unnecessary biopsies. Studies have revealed some correlations between ADC values and hormone receptor status in breast cancers, although substantial variability exists among studies. However, standardization remains challenging. Initiatives such as European Society of Breast Imaging (EUSOBI), Diffusion-Weighted Imaging Screening Trial (DWIST), Quantitative Imaging Biomarkers Alliance (QIBA) have proposed guidelines to ensure consistency in imaging protocols and equipment specifications, addressing variability in ADC measurements across different sites and vendors. Advanced techniques like Intravoxel incoherent motion (IVIM) and non-Gaussian DWI offer insights into tissue microvasculature and microstructure. Despite ongoing challenges, the integration of these advanced MRI techniques shows great promise for improving breast cancer diagnosis, characterization, and treatment planning. Continued research and standardization efforts are crucial for maximizing the potential of breast DWI in enhancing patient care and outcomes.

DOI： 10.1007/s11604-024-01696-z

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Frontiers Media SA  

Introduction

The intravoxel incoherent motion (IVIM) model of diffusion weighted imaging (DWI) provides imaging biomarkers for breast tumor characterization. It has been extensively applied for both diagnostic and prognostic goals in breast cancer, with increasing evidence supporting its clinical relevance. However, variable performance exists in literature owing to the heterogeneity in datasets and quantification methods.

Methods

This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1_st order radiomics of IVIM parameters perfusion fraction (f_p), pseudo-diffusion (D_p) and tissue diffusivity (D_t). Pearson correlation (r) coefficients between software pairs were computed while logistic regression model was implemented to test malignancy detection and assess robustness of the IVIM metrics.

Results

D_t and f_p maps generated from different software showed consistency across platforms while D_p maps were variable. The average correlation between the three software pairs at three different sites for 1_st order radiomics of IVIM parameters were D_tmin/D_tmax/D_tmean/D_tvariance/D_tskew/D_tkurt: 0.791/0.891/0.98/0.815/0.697/0.584; f_pmax/f_pmean/f_pvariance/f_pskew/f_pkurt: 0.615/0.871/0.679/0.541/0.433; D_pmax/D_pmean/D_pvariance/D_pskew/D_pkurt: 0.616/0.56/0.587/0.454/0.51. Correlation between least-squares algorithms were the highest. D_tmean showed highest area under the ROC curve (AUC) with 0.85 and lowest coefficient of variation (CV) with 0.18% for benign and malignant differentiation using logistic regression. D_t metrics were highly diagnostic as well as consistent along with f_p metrics.

Discussion

Multiple 1_st order radiomic features of D_t and f_p obtained from a heterogeneous multi-site breast lesion dataset showed strong software robustness and/or diagnostic utility, supporting their potential consideration in controlled prospective clinical trials.

DOI： 10.3389/fonc.2025.1524634

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OBJECTIVES: Fat-signal suppression is essential for breast diffusion magnetic resonance imaging (or diffusion-weighted MRI, DWI) as the very low diffusion coefficient of fat tends to decrease absolute diffusion coefficient (ADC) values. Among several methods, the STIR (short-tau inversion recovery) method is a popular approach, but signal suppression/attenuation is not specific to fat contrary to other methods such as SPAIR (spectral adiabatic (or attenuated) inversion recovery). This article focuses on those two techniques to illustrate the importance of appropriate fat suppression in breast DWI, briefly presenting the pros and cons of both approaches. METHODS AND RESULTS: We show here through simulation and data acquired in a dedicated breast DWI phantom made of vials with water and various concentrations of polyvinylpyrrolidone (PVP) how ADC values obtained with STIR DWI may be biased toward tissue components with the longest T1 values: ADC values obtained with STIR fat suppression may be over/underestimated depending on the T1 and ADC profile within tissues. This bias is also illustrated in two clinical examples. CONCLUSION: Fat-specific methods should be preferred over STIR for fat-signal suppression in breast DWI, such as SPAIR which also provides a higher sensitivity than STIR for lesion detection. One should remain aware, however, that efficient fat-signal suppression with SPAIR requires good B0 shimming to avoid ADC underestimation from residual fat contamination. CLINICAL RELEVANCE STATEMENT: The spectral adiabatic (or attenuated) inversion recovery (SPAIR) method should be preferred over short-tau inversion recovery (STIR) for fat suppression in breast DWI. KEY POINTS: Fat-signal suppression is essential for breast DWI; the SPAIR method is recommended. Short-tau inversion recovery (STIR) is not specific to fat; as a result, SNR is decreased and ADC values may be over- or underestimated. The STIR fat-suppression method must not be used after the injection of gadolinium-based contrast agents.

DOI： 10.1007/s00330-024-10973-4

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Purpose To evaluate and compare the performance of different artificial intelligence (AI) models in differentiating between benign and malignant breast tumors at diffusion-weighted imaging (DWI), including comparison with radiologist assessments. Materials and Methods In this retrospective study, patients with breast lesions underwent 3-T breast MRI from May 2019 to March 2022. In addition to T1-weighted imaging, T2-weighted imaging, and contrast-enhanced imaging, DWI was performed with five b values (0, 200, 800, 1000, and 1500 sec/mm2). DWI data split into training and tuning and test sets were used for the development and assessment of AI models, including a small two-dimensional (2D) convolutional neural network (CNN), ResNet-18, EfficientNet-B0, and a three-dimensional (3D) CNN. Performance of the DWI-based models in differentiating between benign and malignant breast tumors was compared with that of radiologists assessing standard breast MR images, with diagnostic performance assessed using receiver operating characteristic analysis. The study also examined data augmentation effects (augmentation A: random elastic deformation, augmentation B: random affine transformation and random noise, and augmentation C: mixup) on model performance. Results A total of 334 breast lesions in 293 patients (mean age, 54.9 years ± 14.3 [SD]; all female) were analyzed. The 2D CNN models outperformed the 3D CNN on the test dataset (area under the receiver operating characteristic curve [AUC] with different data augmentation methods: range, 0.83-0.88 vs 0.75-0.76). There was no evidence of a difference in performance between the small 2D CNN with augmentations A and B (AUC: 0.88) and the radiologists (AUC: 0.86) on the test dataset (P = .64). When comparing the small 2D CNN to radiologists, there was no evidence of a difference in specificity (81.4% vs 72.1%, P = .64) or sensitivity (85.9% vs 98.8%, P = .64). Conclusion AI models, particularly a small 2D CNN, showed good performance in differentiating between malignant and benign breast tumors using DWI, without needing manual segmentation. Keywords: MR Imaging, Breast, Comparative Studies, Feature Detection, Diagnosis Supplemental material is available for this article. ©RSNA, 2024.

DOI： 10.1148/ryai.240206

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Magnetic Resonance in Medicine  

PURPOSE: This study investigated the breast lesion conspicuity and apparent diffusion coefficient (ADC) reliability for three different diffusion-weighted imaging (DWI) protocols: spatiotemporal encoding (SPEN), single-shot echo-planar imaging (SS-EPI), and readout segmentation of long variable echo-trains (RESOLVE). METHODS: Sixty-five women suspected of having breast tumors were included in this study, with 44 lesions (36 malignant, 8 benign) analyzed further. Breast MRI was performed on a 3 Tesla (3T) system (MAGNETOM Prisma, Siemens) equipped with a dedicated 18-channel breast array coil for a phantom and patients. Three DWI protocols-SPEN, SS-EPI, and RESOLVE-were used. SS-EPI was acquired with an in-plane resolution of 2 × 2 mm2, a slice thickness of 3 mm, and b-values of 0 and 1000 s/mm2. SPEN had a higher in-plane resolution of 1 × 1 mm2, a slice thickness of 1.5 mm, and b-values of 0, 850, and 1500 s/mm2. RESOLVE was acquired with an in-plane resolution of 1 × 1 mm2, a slice thickness of 1.5 mm, and b-values of 0 and 850 s/mm2. Lesion conspicuity and ADC values were evaluated. RESULTS: The average lesion conspicuity scores were significantly higher for RESOLVE (3.54 ± 0.65) than for SPEN (3.07 ± 0.91) or SS-EPI (2.48 ± 0.78) (P < 0.01). The SPEN score was significantly higher than the SS-EPI score (P < 0.01). Phantom measurements indicated marginally lower ADC values for SPEN compared to SS-EPI and RESOLVE across all concentrations. The results revealed that SPEN (b = 0, 850, 1500 sec/mm2) yielded significantly lower ADC values compared to SPEN (b = 0, 850 sec/mm2) in malignant lesions (P < 0.01), with no significant difference observed between SPEN (b = 0, 850 sec/mm2), SS-EPI, and RESOLVE. For benign lesions, no significant difference in ADC values was found between SPEN (b = 0, 850 sec/mm2), SPEN (b = 0, 850, 1500 sec/mm2), SS-EPI, and RESOLVE. CONCLUSION: RESOLVE provided the highest lesion conspicuity, and ADC values in breast lesions were not significantly different among sequences ranging b values 850-1000 sec/mm2. SPEN with higher b-values (0, 850, 1500 vs. 0, 850 sec/mm2) yielded significantly lower ADC values in malignant lesions, highlighting the importance of b-value selection in ADC quantification.

DOI： 10.2463/mrms.tn.2024-0089

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Magnetic Resonance in Medicine  

<p>Purpose: To assess the institutional variability in ultrafast dynamic contrast-enhanced (UF-DCE) breast MRI using time-resolved angiography with stochastic trajectories (TWIST)-volumetric interpolated breath-hold examination (VIBE) and compressed sensing (CS)-VIBE sequences acquired at 2 different institutions with different patient populations and contrast injection protocols.</p><p>Methods: UF-DCE MR images of 18 patients from site A acquired using a TWIST-VIBE sequence, and UF-DCE MR images of 18 patients from site B acquired with a CS-VIBE sequence, were retrospectively evaluated and compared. The 2-site patient cohort was matched for patient age, background parenchymal enhancement, malignancy or benignity, and lesion size. Qualitative assessments included noise, blurring, poor fat suppression, aliasing artifact, motion artifact, lesion conspicuity, lesion morphology, time-intensity-curve smoothness, and vessel delineation. For quantitative assessment, the bolus arrival time was evaluated for each lesion, and its diagnostic performance in discriminating between benign and malignant lesions was examined using receiver operating characteristics analysis.</p><p>Results: Thirteen malignant and five benign lesions were included from each site. Qualitative evaluation revealed that poor fat suppression and aliasing artifacts were visible in images from site A with TWIST-VIBE (<i>P</i> = 0.004 and <i>P</i> < 0.001), whereas motion artifacts were present in images from site B with CS-VIBE (<i>P</i> = 0.04). Lesion morphology assessments (<i>P </i>< 0.001) and vessel delineation (<i>P</i> < 0.001) were superior for images from site B with CS-VIBE. Bolus arrival time was significantly longer with TWIST-VIBE than with CS-VIBE, for both benign and malignant lesions (<i>P</i> < 0.001). The area under the receiver operating characteristics curve was 0.55 for site A and 0.69 for site B (<i>P</i> = 0.39).</p><p>Conclusion: Both acquisitions allowed evaluation of breast lesions with good lesion conspicuity and time-intensity-curve smoothness, whereas CS-VIBE was superior to TWIST-VIBE for morphological evaluation of breast lesions and depiction of blood vessels in the breast. Injection rate appears to have a significant impact on semi-quantitative parameters derived from UF-DCE MRI.</p>

DOI： 10.2463/mrms.mp.2024-0152

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CiNii Research

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Magnetic Resonance in Medicine  

<p>Breast MRI has become essential for diagnosing and managing breast diseases. MRI interpretation has traditionally relied on subjective assessment according to Breast Imaging-Reporting and Data System, but recent advances emphasize the value of quantitative MRI data as objective imaging biomarkers. Techniques such as dynamic contrast-enhanced MRI (DCE-MRI), including ultrafast DCE-MRI and diffusion-weighted imaging are the most established for quantitative analysis, yet their routine application is limited by a lack of standardization in imaging protocols. Recent efforts focus on improving reproducibility, with studies showing that significant changes in the apparent diffusion coefficient can reliably indicate biological alterations in breast lesions. Emerging quantitative MRI approaches including synthetic MRI and fat imaging show promise in lesion characterization and treatment response assessment but require further validation and harmonization before widespread clinical use. For methodological aspect, radiomics has demonstrated strong diagnostic and predictive capabilities in breast MRI research. Quantitative approach for background parenchymal enhancement indicated its association with future breast cancer incidence or recurrence. Continued efforts to standardize and validate quantitative MRI parameters are crucial to fully integrating these tools into routine breast imaging practice.</p>

DOI： 10.2463/mrms.rev.2025-0085

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DOI： 10.1007/s11604-024-01669-2

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Radiological Society of North America (RSNA)  

DOI： 10.1148/radiol.242642

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Magnetic Resonance in Medicine  

<p>　Three DW protocols (SPEN, SS-EPI, RESOLVE) were evaluated by breast radiologists and medical students using a semi-quantitative scoring system. Additionally, a breast-specific phantom, encompassing typical malignant and benign breast lesions, was used to measure the ADC values in each DWI protocol. The ADC values were also measured for malignant lesions. SPEN sequences demonstrated significantly higher conspicuity scores than conventional DWI. Moreover, RESOLVE exhibited significantly higher scores compared to both SPEN and conventional DWI.</p>

DOI： 10.2463/jjmrm.2024-1811

CiNii Research

Language：Japanese   Publisher：(一社)日本磁気共鳴医学会  

Language：English   Publishing type：Research paper (scientific journal)  

This study systematically reviewed the role of diffusion-weighted imaging (DWI) in the assessment of molecular prognostic biomarkers in breast cancer, focusing on the correlation of apparent diffusion coefficient (ADC) with hormone receptor status and prognostic biomarkers. Our meta-analysis includes data from 52 studies examining ADC values in relation to estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki-67 status. The results indicated significant differences in ADC values among different receptor statuses, with ER-positive, PgR-positive, HER2-negative, and Ki-67-positive tumors having lower ADC values compared to their negative counterparts. This study also highlights the potential of advanced DWI techniques such as intravoxel incoherent motion and non-Gaussian DWI to provide additional insights beyond ADC. Despite these promising findings, the high heterogeneity among the studies underscores the need for standardized DWI protocols to improve their clinical utility in breast cancer management.

DOI： 10.3348/kjr.2023.1188

Open Access

Web of Science

Scopus

PubMed

Language：Japanese   Publisher：(一社)日本乳癌学会  

Language：Japanese   Publisher：(一社)日本乳癌学会  

Language：English   Publishing type：Research paper (scientific journal)  

PURPOSE: We aimed to investigate the changes in intravoxel incoherent motion (IVIM) and diffusion parameters between in vivo and post-mortem conditions and the time dependency of these parameters using two different mouse tumor models with different vessel lumen sizes. METHODS: Six B16 and six MDA-MB-231 xenograft mice were scanned using 7 Tesla MRI under both in vivo/post-mortem conditions. Diffusion weighted imaging with 17 b-values (0-3000 s/mm2) were obtained at two diffusion times (9 and 27.6 ms). The shifted apparent diffusion coefficient (sADC) using 2 b-values (200 and 1500 s/mm2), non-Gaussian diffusion and IVIM parameters (ADC0, K, fIVIM) were estimated at each of the diffusion times. The results were evaluated by repeated measures two-way analysis of variance and post hoc Bonferroni test. RESULTS: In B16 tumors, fIVIM significantly decreased with post-mortem conditions (from 12.6 ± 6.5% to 5.2 ± 1.9%, P < 0.05 at long diffusion time; from 11.0 ± 2.4% to 4.6 ± 2.7%, P < 0.05 at short diffusion time). In MDA-MB-231 tumors, fIVIM also significantly decreased (from 8.8 ± 3.8% to 2.6 ± 1.1%, P < 0.05 at long; from 7.9 ± 5.4% to 2.9 ± 1.1%, P < 0.05 at short). No diffusion time dependency was observed (P = 0.59 in B16 and P = 0.77 in MDA-MB-231). The sADC and ADC0 values tended to decrease and the K value tended to increase after sacrificing and when increasing the diffusion time. CONCLUSION: The fIVIM values dropped after sacrificing, confirming that IVIM MRI is a promising quantitative parameter to evaluate blood microcirculation. The presence of residual post-mortem fIVIM values suggested that the influence of water molecule diffusion in the blood lumen may contribute to the IVIM effect. Diffusion MRI parameter's time dependency and those changes after sacrificing could possibly provide additional insights into diffusion hindrance mechanisms.

DOI： 10.2463/mrms.mp.2023-0078

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1148/radiol.241058

Web of Science

Scopus

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1002/jmri.29311

PubMed

Language：English  

DOI： 10.1148/radiol.240125

PubMed

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Magnetic Resonance in Medicine  

PURPOSE: The 8th edition of the American Joint Committee on Cancer staging system included the depth of invasion (DOI) for the T classification of oral cancer. However, no standardized method has been established to clinically measure the DOI. This study aimed to investigate the accuracy of MRI-based DOI for oral tongue squamous cell carcinoma (OTSCC) in each MRI sequence. METHODS: We enrolled 49 patients with histologically proven OTSCC, treated surgically between April 2017 and February 2021. We divided the DOI into three groups using 5 and 10 mm, the thresholds for determining the T stage, and retrospectively evaluated the agreement between MRI-based DOI and pathological DOI (pDOI) for each MRI sequence, axial T1-weighted imaging (T1WI), T2-weighted imaging with fat suppression (FS-T2WI), contrast-enhanced T1WI with fat suppression (CE-T1WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. We also divided the DOI into two groups using 3 mm, the threshold for considering elective neck dissection, and evaluated the overestimation rate of MRI-based DOI in lesions with pDOI ≤ 3 mm. RESULTS: With 5-mm and 10-mm divisions, the accuracy of the DOI assessment was highest on DWI (0.82, weighted kappa = 0.85). With a 3-mm division, the accuracy was also highest on DWI (0.87, kappa = 0.73). The overestimation rate of the MRI-based DOI in lesions with pDOI ≤ 3 mm was lowest on DWI (27.8%). CONCLUSION: DOI on DWI exhibits a comparatively higher rate of concordance with pDOI. DWI may be more useful than other MRI sequences in evaluating the DOI of OTSCC.

DOI： 10.2463/mrms.mp.2023-0137

PubMed

Event date： 2025.8

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Presentation type：Poster presentation  

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Language：English   Presentation type：Poster presentation  

Event date： 2024.11 - 2024.12

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Event date： 2014.5

Event date： 2011.5

Presentation type：Oral presentation (invited, special)  

Presentation type：Oral presentation (invited, special)  

Presentation type：Symposium, workshop panel (nominated)  

Presentation type：Poster presentation  

Language：Japanese   Presentation type：Symposium, workshop panel (nominated)  

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc.