Diffusion-weighted Magnetic Resonance Imaging Research Articles

Impact of diffusion-weighted imaging on agreement between radiologists and non-radiologists in musculoskeletal tumor imaging using magnetic resonance.

BackgroundDiffusion-weighted imaging (DWI) is widely used in neuroradiology or abdominal imaging but not yet implemented in the diagnosis of musculoskeletal tumors. AimThis study aimed to evaluate how including diffusion imaging in the MRI protocol for patients with musculoskeletal tumors affects the agreement between radiologists and non-radiologist. MethodsThirty-nine patients with musculoskeletal tumors (Ewing sarcoma, osteosarcoma, and benign tumors) consulted at our institution were included. Three raters with different experience levels evaluated examinations blinded to all clinical data. The final diagnosis was determined by consensus. MRI examinations were split into 1) conventional sequences and 2) conventional sequences combined with DWI. We evaluated the presence or absence of diffusion restriction, solid nature, necrosis, deep localization, and diameter >4cm as known radiological markers of malignancy. Agreement between raters was evaluated using Gwet’s AC1 coefficients and interpreted according to Landis and Koch. ResultsThe lowest agreement was for diffusion restriction in both groups of raters. Agreement among all raters ranged from 0.51 to 0.945, indicating moderate to almost perfect agreement, and 0.772 to 0.965 among only radiologists indicating substantial to almost perfect agreement. ConclusionThe agreement in evaluating diffusion-weighted MRI sequences was lower than that for conventional MRI sequences, both among radiologists and non-radiologist and among radiologists alone. This indicates that assessing diffusion imaging is more challenging, and experience may impact the agreement.

Effectiveness of diffusion-weighted magnetic resonance imaging (DW-MRI) in the differentiation of thyroid nodules

Effectiveness of diffusion-weighted magnetic resonance imaging (DW-MRI) in the differentiation of thyroid nodules

The Added Value of Apparent Diffusion Coefficient and Histogram Analysis in Assessing Treatment Response of Locally Advanced Cervical Cancer

Objective The aim of the study is to assess the diagnostic performance of quantitative analysis of diffusion-weighted imaging in assessing treatment response in cervical cancer patients. Methods A retrospective analysis was done for 50 patients with locally advanced cervical cancer who received concurrent chemoradiotherapy and underwent magnetic resonance imaging and diffusion-weighted imaging. Treatment response was classified into 4 categories according to RECIST criteria 6 months after therapy completion. Apparent diffusion coefficient (ADC) values were measured using both region of interest (ROI) ADC and whole lesion (WL) ADC histogram for all cases at both baseline pretreatment and posttreatment Magnetic resonance imaging studies. Changes in ADC values were calculated and compared between groups. Results The percentage change of ROI-ADCmean at a cutoff value of >20 had excellent discrimination of responders versus nonresponders, while the percentage change of WL-ADCmean, ADCmin, and ADCmax at cutoff values of >12.5, >35.8, and > 19.6 had acceptable discrimination of responders versus nonresponders. Logistic regression analysis revealed that only baseline WL ADCmin was a statistically significant independent predictor of response. Cancer cervix patients with baseline ADCmin < or equal to 0.73 have 12.1 times higher odds of exhibiting a response. Conclusions The percentage change of ROI-ADCmean and WL histogram ADCmean values after concurrent chemoradiotherapy can predict response. Pretreatment WL histogram ADCmin was a statistically significant independent predictor of posttherapy response.

Synaptic Density in Early Stages of Psychosis and Clinical High Risk.

Synaptic dysfunction is involved in schizophrenia pathophysiology. However, whether in vivo synaptic density is reduced in early stages of psychosis, including its high-risk states, remains unclear. To investigate whether synaptic density (synaptic vesicle glycoprotein 2A [SV2A] binding potential) is reduced in first-episode psychosis (FEP) and in clinical high risk (CHR) and investigate the effect of cannabis use on synaptic density and examine its relationship with psychotic symptoms and gray matter microstructure across groups. This cross-sectional study was performed in a tertiary care psychiatric hospital from July 2021 to October 2023. Participants were patients with antipsychotic-free or minimally exposed FEP or CHR and healthy controls with a clean urine drug screen (except cannabis). Synaptic density was quantified with dynamic 90-minute [18F]SynVesT-1 positron emission tomography (PET) scans across prioritized brain regions of interest (ROIs) delineated in individual magnetic resonance images (MRIs). Cannabis use was confirmed with urine drug screens. Gray matter microstructure was assessed using diffusion-weighted MRI to estimate neurite density. A total of 49 participants were included, including 16 patients with FEP (mean [SD] age, 26.1 [4.6] years; 9 males and 7 females), 17 patients at CHR (mean [SD] age, 21.2 [3.5] years; 8 males and 9 females), and 16 healthy controls (mean [SD] age, 23.4 [3.6] years; 7 males and 9 females). Synaptic density was significantly different between groups (F2,273 = 4.02, P = .02, Cohen F = 0.17; ROI: F5,273 = 360.18, P < .01, Cohen F = 2.55) with a group × ROI interaction (F10,273 = 2.67, P < .01, Cohen F = 0.32). Synaptic density was lower in cannabis users (F1,272 = 5.31, P = .02, Cohen F = 0.14). Lower synaptic density across groups was associated with more negative symptoms (Positive and Negative Syndrome Scale negative scores: F1,81 = 4.31, P = .04, Cohen F = 0.23; Scale of Psychosis-Risk Symptoms negative scores: F1,90 = 4.12, P = .04, Cohen F = 0.21). SV2A binding potential was significantly associated with neurite density index (F1,138 = 6.76, P = .01, Cohen F = 0.22). This study found that synaptic density reductions were present during the early stages of psychosis and its risk states and associated with negative symptoms. The implications of SV2A for negative symptoms in psychosis and CHR warrant further investigation. Future studies should investigate the impact of cannabis use on synaptic density in CHR longitudinally.

Diffusion MRI with machine learning

Abstract Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high intersession and interscanner variability in the data, as well as intersubject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

Added-value of dynamic contrast-enhanced MRI to conventional MRI for the differentiation between inflammatory myofibroblastic tumor and squamous cell carcinoma in the sinonasal region.

The purpose of this study was to evaluate the additional value of dynamic contrast-enhanced (DCE) MRI and diffusion weighted MRI (DWI) in differentiation between inflammatory myofibroblastic tumor (IMT) and squamous cell carcinoma (SCC) in the sinonasal cavity. Patients with pathologically proven IMT and SCC in the sinonasal region were enrolled in this retrospective study. All participants underwent conventional MRI and dynamic contrast-enhanced MRI, while a subset of them performed DWI. All the MRI parameters were independently analyzed by two investigators. This retrospective study included 21 patients with IMT and 55 patients with SCC. Significant differences were found in the conventional MR imaging features including mass margin, T2 signal intensity and track sign of maxillary (p < 0.05). For DCE-MRI features, significant differences were found in progressive centripetal continual enhancement and CImax (p < 0.001 and p = 0.026, respectively). A marginal significant difference was found in ADC values between IMT (0.86 ± 0.59) and SCC (1.14 ± 0.25) (p = 0.061). The conventional MRI analysis revealed that the combination of mass margin and track sign of maxillary yielded an accuracy of 81.6%. Using a combination of progressive centripetal continual enhancement on DCE-MRI and track sign of maxillary in multivariate logistic regression analysis, the accuracy was elevated to 92.1%. The incorporation of DCE-MRI features into conventional MRI showed improved diagnostic performance in differentiating IMT from SCC in the sinonasal region. The novel progressive centripetal continual enhancement on DCE-MRI is the most effective feature of IMT.

NIMG-74. A NOVEL APPROACH FOR PERSONALIZED RADIOTHERAPY TARGET VOLUME DEFINITION USING METABOLIC MRI AND DEEP LEARNING-DRIVEN PREDICTIONS OF GLIOBLASTOMA RECURRENCE

Abstract BACKGROUND Machine-learning models have demonstrated great promise in predicting tumor infiltration beyond anatomical margins in glioblastoma. Yet standard-of-care (SOC) radiation therapy (RT) planning only utilizes a uniform, isotropic 2cm-expansion of the T1-post-contrast-lesion or T2-lesion to generate a clinical target volume (CTV), without considering heterogeneity in tumor infiltration. We hypothesize that using a novel deep-learning approach to predict regions of tumor progression with metabolic and diffusion-weighted MRI will result in personalized CTVs that are more sensitive to detecting normal-appearing infiltrating tumor beyond the T2-lesion, sparing more healthy brain compared to SOC-CTVs. METHODS Anatomical, diffusion-weighted, and metabolic MRI from 101 patients with glioblastoma, acquired post-surgery but prior to chemoradiation, were retrospectively used to predict regions of new contrast-enhancement or T2-hyperintensity at confirmed progression. A segmentation-based predictive deep-learning approach with personalized loss-functions and evaluation metrics (“Progression-Coverage-Coefficient”, PCC) that weight Dice coefficient terms by tumor size was employed with a 66/17/18 train/validation/test split of patients and the resulting DL-CTV compared to two SOC-CTVs: 1) the RTOG[T2-FLAIR+2cm]-CTV, 2) the EORTC[T1c+2cm]-CTV. RESULTS Our best deep-learning-CTV trained using anatomical, diffusion, and metabolic MRI achieved significantly improved median test specificity (0.87 vs. 0.77; p&amp;lt;0.005) and comparable sensitivity (0.92 vs. 0.95; p=0.1) and PCC (0.81 vs. 0.79; p=0.1) to the SOC RTOG[T2-FLAIR+2cm]-CTV, sparing more normal brain tissue. Compared to the more conservative EORTC[T1c+2cm]-CTV, our approach had significantly higher test sensitivity (0.92 vs. 0.85; p&amp;lt;0.00001) and PCC (0.81 vs. 0.77; p=0.003) in covering the progressed lesion, while maintaining similar specificity (0.87 vs. 0.89; p=0.3). Test performance dropped by 9.6% and 12.4% when removing metabolic and both metabolic and diffusion metrics, respectively (p&amp;lt;0.001). CONCLUSION This study demonstrates the benefit of using metabolic and diffusion MRI with deep learning for personalized RT planning by targeting regions of anatomically normal infiltrative disease that are highly likely to progress, while reducing treatment to normal brain.

Comprehensive Management of Cholesteatoma in Otitis Media: Diagnostic Challenges, Imaging Advances, and Surgical Outcome

Background: This study presents a comprehensive analysis of cholesteatoma of the middle ear, focusing on its clinical presentation, diagnostic imaging, and treatment outcomes. Cholesteatomas are defined by the keratinized squamous epithelium within the middle ear, leading to significant bone erosion, often affecting the ossicular chain and surrounding structures. Methods: The study explores various mechanisms involved in cholesteatoma progression, including enzymatic lysis, inflammatory responses, and neurotrophic disturbances. The study conducted a retrospective clinical and statistical review of 580 patients over a 20-year period (2003–2023), highlighting the role of advanced imaging, including computed tomography (CT) and diffusion-weighted magnetic resonance imaging (DWI), in preoperative planning and postoperative follow-up. Results: Findings revealed that early detection and intervention are crucial in preventing severe complications such as intracranial infection and hearing loss. Surgical treatment primarily involved tympanoplasty and mastoidectomy, with a recurrence rate of 1.55% within two years. The study underscores the importance of integrating imaging advancements into clinical decision-making to enhance patient outcomes and suggests further investigation into molecular mechanisms underlying cholesteatoma progression and recurrence. Histopathological and microbiological analysis was performed to identify pathological patterns and microbial agents. Conclusions: The study highlights the importance of early diagnosis and intervention to prevent complications such as intracranial infections and permanent hearing loss, while also emphasizing the role of advanced imaging techniques in the management and long-term monitoring of cholesteatoma patients.

NIMG-76. INCORPORATING IMAGING FEATURES OF NORMAL BRAIN TO ENHANCE SPATIALLY DIFFERENTIATING TREATMENT-INDUCED EFFECTS FROM RECURRENT GLIOMA WITH AI

Abstract INTRODUCTION Although AI-based approaches have been applied to discriminate between tumor recurrence (rTumor) and treatment-induced effects (TxE) from chemoradiotherapy in patients with recurrent glioma, these models typically either do not account for within-lesion mixtures of rTumor and TxE or generalize to independent test-sets with &amp;gt;80% accuracy. We hypothesize that incorporating imaging features of normal-appearing brain (NAB) into existing AI-based models that leverage tissue-samples with known locations on MRI and physiological imaging will significantly improve performance for distinguishing regions of TxE from rTumor and allow for visualization of rTumor beyond the T2-lesion. METHODS Using 254 pathology-confirmed tissue-samples with coordinates on diffusion-weighted, perfusion-weighted, and anatomical MRI from 135 glioma suspected of progression, we modified previously-developed binary, AI-based prediction models to learn multi-class targets consisting of rTumor, TxE, and contralateral NAB-regions (3/patient) with imaging signatures of CSF, white-matter, and grey-matter (659 total 10mm-isotropic samples). The multi-class ensemble model was trained with 4 unique repeats of constrained-5-fold-cross-validation and evaluated on a held-out test set (30 patients). This process was repeated 20 times, and the performance was compared to corresponding binary AI-based models. RESULTS NAB was easiest to distinguish from individual and combined classes (AUC-ROCs&amp;gt;0.98). Multi-class model performance for predicting TxE increased by 27% to 0.93±0.03 (p&amp;lt;0.00001) when discriminating between NAB and rTumor as compared to just rTumor with the binary models. While TxE vs rTumor alone was most difficult to predict with the multi-class model (AUC-ROC=0.73) achieving equivalent performance to binary models, there was 67% less variance in test performance. This approach allowed for the quantification of whole-brain probability maps of rTumor, TxE, and NAB along with a combined 3-channel colormap, facilitating the visualization of rTumor predictions beyond the T2-lesion. CONCLUSIONS Including NAB imaging features improved performance of AI-based models of rTumor/TxE, while expanding the predicted area to produce more interpretable whole-brain probability maps. Current work is evaluating their utility in prospectively guiding sampling of rTumor tissue and predicting survival.

Fast BET Based on Pre-Processing Evolution Using Global Mean Inter-Vertex Distance of Deformable Surface for MRI Brain Extraction

As a method based on the deformable surface evolution, brain extraction tools (BET) is widely used for brain extraction on cranial 3D magnetic resonance (MR) images. BET iteratively applies an evolution model depending on local parameters to the deformable surface until it reaches the brain border. In this paper, we propose a modified BET based on the fast model introducing a global parameter, the global mean inter-vertex distance of deformable surface. The surface evolution consists of two steps driven by two models: pre-processing step by the fast model and major step by the original model. We demonstrated our method in the experiments with T1-weighted and diffusion weighted MR images of 19 normal subjects. The modified BET converged within 300 iterations less than half of about 1000 iterations in the original BET, while reducing substantially the false negative regions and self-intersections. The proposed method may be efficient for high resolution images and large-scale studies.

Performance of high-resolution diffusion-weighted magnetic resonance imaging for detecting clinically occult early breast cancers: a multi-reader study.

To compare mammography, breast ultrasound (US), high-resolution diffusion-weighted magnetic resonance imaging (DW-MRI), dynamic contrast-enhanced breast MRI (DCE-MRI), and their combinations for detecting clinically occult early breast cancers (EBCs), including ductal carcinoma in situ (DCIS). Three hundred and three consecutive women with screening imaging-detected early breast cancers (60 pure DCIS, 36 DCIS with microinvasion, and 207 invasive carcinoma less than 20mm) who underwent breast MRI at 3T including DW-MRI (b-values of 0, 800 and 1200s/mm2; in-plane resolution, 1.1 × 1.1 mm2 or 1.3 × 1.3 mm2; section thickness, 3mm) were retrospectively reviewed. Three radiologists independently reviewed each examination. Statistical analysis included Chi-square test, McNemar test for comparison of cancer detection rates, and Fleiss' Kappa for interreader agreement. Mixed-effect logistic regression analysis was employed to evaluate factors associated with cancer detection on DW-MRI. The overall cancer detection rates were 54.8% on mammography, 71.0% on breast US, 81.5% on DW-MRI, and 87.1% on DCE-MRI. On McNemar test, DW-MRI detected more cancers than mammography (adjusted p < 0.001), and its combination with mammography showed a similar cancer detection rate to DCE-MRI combined with mammography (adjusted p = 0.808). On multivariable analysis, histologic type, lesion size, ADC and CNR on DW-MRI were independent factors for cancer detection on DW-MRI. The interreader agreement for cancer detection was moderate to substantial (Fleiss' kappa: 0.52-0.65) across each modality. High-resolution DW-MRI plus mammography showed comparable cancer detection rate to DCE-MRI plus mammography for detecting clinically occult EBCs including DCIS.

Astrocyte aquaporin mediates a tonic water efflux maintaining brain homeostasis.

Brain water homeostasis not only provides a physical protection, but also determines the diffusion of chemical molecules key for information processing and metabolic stability. As a major type of glia in brain parenchyma, astrocytes are the dominant cell type expressing aquaporin water channel. How astrocyte aquaporin contributes to brain water homeostasis in basal physiology remains to be understood. We report that astrocyte aquaporin 4 (AQP4) mediates a tonic water efflux in basal conditions. Acute inhibition of astrocyte AQP4 leads to intracellular water accumulation as optically resolved by fluorescence-translated imaging in acute brain slices, and in vivo by fiber photometry in mobile mice. We then show that aquaporin-mediated constant water efflux maintains astrocyte volume and osmotic equilibrium, astrocyte and neuron Ca2+ signaling, and extracellular space remodeling during optogenetically induced cortical spreading depression. Using diffusion-weighted magnetic resonance imaging (DW-MRI), we observed that in vivo inhibition of AQP4 water efflux heterogeneously disturbs brain water homeostasis in a region-dependent manner. Our data suggest that astrocyte aquaporin, though bidirectional in nature, mediates a tonic water outflow to sustain cellular and environmental equilibrium in brain parenchyma.

Astrocyte aquaporin mediates a tonic water efflux maintaining brain homeostasis

Brain water homeostasis not only provides a physical protection, but also determines the diffusion of chemical molecules key for information processing and metabolic stability. As a major type of glia in brain parenchyma, astrocytes are the dominant cell type expressing aquaporin water channel. How astrocyte aquaporin contributes to brain water homeostasis in basal physiology remains to be understood. We report that astrocyte aquaporin 4 (AQP4) mediates a tonic water efflux in basal conditions. Acute inhibition of astrocyte AQP4 leads to intracellular water accumulation as optically resolved by fluorescence-translated imaging in acute brain slices, and in vivo by fiber photometry in mobile mice. We then show that aquaporin-mediated constant water efflux maintains astrocyte volume and osmotic equilibrium, astrocyte and neuron Ca2+ signaling, and extracellular space remodeling during optogenetically induced cortical spreading depression. Using diffusion-weighted magnetic resonance imaging (DW-MRI), we observed that in vivo inhibition of AQP4 water efflux heterogeneously disturbs brain water homeostasis in a region-dependent manner. Our data suggest that astrocyte aquaporin, though bidirectional in nature, mediates a tonic water outflow to sustain cellular and environmental equilibrium in brain parenchyma.

Characterisation of Paediatric Neuroblastic Tumours by Quantitative Structural and Diffusion-Weighted MRI

Purpose: To determine the diagnostic accuracy of quantitative diffusion-weighted (DW) MRI apparent diffusion coefficient (ADC) and tumour volumes to differentiate between malignant (neuroblastoma (NB)) and benign types of neuroblastic tumours (ganglioneuroma (GN) and ganglioneuroblastoma (GNB)) using different region-of-interest (ROI) sizes. Materials and Methods: This single-centre retrospective study included malignant and benign paediatric neuroblastic tumours that had undergone DW MRI at diagnosis. The outcome was diagnostic accuracy of the tumour volume from structural and ADC DW MRI, in comparison to histopathology (reference standard). Results: Data from 40 patients (NB, n = 24; GNB, n = 6; GN, n = 10), 18 (45%) females and 22 (55%) males, with a median age at diagnosis of 21 months (NB), 64 months (GNB), and 133 months (GN), respectively, ranging from 0 to 193 months, were evaluated. The area under the receiver operating characteristic (AUROC) curve for ADC for discriminating between neuroblastic tumours’ histopathology for a small ROI was 0.86 (95% CI: 0.75–0.98), and for a large ROI, 0.83 (95% CI: 0.71–0.96). An ADC cut-off value of 1.06 × 10−3 mm2/s was able to distinguish malignant from benign tumours with 83% (68–98%) sensitivity and 75% (95% CI: 54–98%) specificity. Tumour volume was not indicative of malignant vs. benign tumour diagnosis. Conclusions: In this study, both small and large ROIs used to derive ADC DW MRI metrics demonstrated high accuracy to differentiate malignant from benign neuroblastic tumours, with the ADC AUROC for the averaged multiple small ROIs being slightly greater than that of large ROIs, but with overlapping 95% CIs. This should be taken into consideration for standardisation of ROI-related data analysis by international initiatives.

Diffusion weighted MRI and neutrophil lymphocyte ratio non-invasively predict infection in pancreatic necrosis: a pilot study.

Infected pancreatic necrosis (IPN) is a major determinant of mortality in acute pancreatitis (AP). Non-invasive diagnosis of IPN could guide the intervention in AP. We aimed to investigate the role of non-invasive methods like diffusion weighted magnetic resonance imaging (DW-MRI) and clinico-laboratory parameters as predictors of IPN. Prospective evaluation for predictors of IPN by diffusion restriction (DR) on DW-MRI and clinico-laboratory parameters was performed. Out of 39 patients included, 31 were analysed after exclusion. Twenty-six (83.8%) patients had moderately severe AP, and the rest had severe disease. They were categorized into Group A: patients with documented infection after intervention (n = 17) and Group B: successfully managed without intervention or negative culture after intervention (n = 14). On univariate analysis, Group A had significantly more incidence of fever (P = 0.020), persistent unwellness (P = 0.003), elevated neutrophil count (P = 0.007), lymphocyte count (P = 0.007), neutrophil lymphocyte ratio (NLR) (P = 0.028), DR on DW-MRI (P = 0.001) and low apparent diffusion coefficient (ADC) (P = 0.086). Multivariate analysis revealed DR on DW-MRI (P = 0.004) and NLR (P = 0.035) as significant predictors of IPN, among other factors. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DW-MRI were 94.1%, 78.6%, 91.66%, and 84.21%, respectively. The area under curve of NLR on the ROC plot was 0.85 and the best cutoff was >3.5, with sensitivity, specificity, PPV, and NPV of 70.6%, 78.6%, 80%, and 68.7% respectively. DW-MRI and NLR are promising non-invasive tools for accurate prediction of IPN and hence can guide the need for intervention in acute pancreatitis.

Are background breast parenchymal features on preoperative breast MRI associated with disease-free survival in patients with invasive breast cancer?

To evaluate whether breast parenchymal features of the contralateral breast on preoperative MRI are associated with primary breast cancer characteristics and disease-free survival (DFS) in women with invasive breast cancer. Women with newly diagnosed invasive breast cancer who underwent preoperative breast MRI followed by surgery were retrospectively evaluated. Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI and background diffusion signal (BDS) on diffusion-weighted MRI of the contralateral breast were qualitatively assessed using a four-category scale: minimal, mild, moderate, or marked. Primary breast cancer characteristics were compared based on the degree of BPE or BDS. Cox proportional hazards models were used to evaluate the association between MRI parenchymal features and DFS after adjusting for clinicopathologic features. A total of 515 women (mean age, 54years) were included. Of whom, 46 (8.9%) patients who developed disease recurrence at a median follow-up of 60months were observed. A high level (moderate/marked) of BPE or BDS was associated with younger age (≤ 45) and premenopausal status (all P < 0.05) compared to a low level (minimal/mild), but it was not associated with primary cancer characteristics such as tumor stage, grade, or subtype. Multivariable Cox proportional hazards analysis demonstrated that larger tumor size (> 2cm) (hazard ratio [HR], 3.877; P < . 001), triple-negative subtype (HR, 2.440; P = .013), and axillary node metastasis (HR, 1.823; P = .049) were associated with worse DFS. No associations were observed between background parenchymal features and disease outcomes. MRI parenchymal features, including BPE and BDS, of the contralateral breast showed no associations with primary breast cancer characteristics or DFS in women with invasive breast cancer.

Non-invasive imaging modalities for diagnosing pulsatile tinnitus: a comprehensive review and recommended imaging algorithm.

Pulsatile tinnitus (PT) is a challenging diagnostic condition arising from various vascular, neoplastic, and systemic disorders. Non-invasive imaging is essential for identifying underlying causes while minimizing risks of invasive diagnostic angiography. Although no consensus exists on the primary imaging modality for PT and currently CT, ultrasound, and MRI are used in the diagnostic pathway, MRI is increasingly preferred as the first-line screening test for its diagnostic efficacy and safety. MRI protocols such as time-of-flight, magnetic resonance angiography, diffusion-weighted imaging, and arterial spin labeling can identify serious causes, including vascular shunting lesions, venous sinus stenosis, and tumors. In this narrative review of the current literature we discuss the benefits and limitations of various non-invasive imaging modalities in identifying the characteristic imaging findings of the most common causes of PT and also provide an algorithm that clinicians can use to guide the imaging evaluation.

Subcortical Alterations in Newly Diagnosed Epilepsy and Associated Changes in Brain Connectivity and Cognition.

Patients with chronic focal epilepsy commonly exhibit subcortical atrophy, particularly of the thalamus. The timing of these alterations remains uncertain, though preliminary evidence suggests that observable changes may already be present at diagnosis. It is also not yet known how these morphological changes are linked to the coherence of white matter pathways throughout the brain, or to neuropsychological function often compromised before antiseizure medication treatment. This study investigates localized atrophy in subcortical regions using surface shape analysis in individuals with newly diagnosed focal epilepsy (NDfE) and assesses their implications on brain connectivity and cognitive function. We collected structural (T1w) and diffusion-weighted MRI and neuropsychological data from 104 patients with NDfE and 45 healthy controls (HCs) matched for age, sex, and education. A vertex-based shape analysis was performed on subcortical structures to compare patients with NDfE and HC, adjusting for age, sex, and intracranial volume. The mean deformation of significance areas (pcor < 0.05) was used to identify white matter pathways associated with overall shape alterations in patients relative to controls using correlational tractography. Additionally, the relationship between significant subcortical shape values and neuropsychological outcomes was evaluated using a generalized canonical correlation approach. Shape analysis revealed bilateral focal inward deformation (a proxy for localized atrophy) in anterior areas of the right and left thalamus and right pallidum in patients with NDfE compared to HC (FWE corrected). No structures showed areas of outward deformation in patients. The connectometry analysis revealed that fractional anisotropy (FA) was positively correlated with thalamic and pallidal shape deformation, that is, reduced FA was associated with inward deformation in tracts proximal to and or connecting with the thalamus including the fornix, frontal, parahippocampal, and corticothalamic pathways. Thalamic and pallidal shape changes were also related to increased depression and anxiety and reduced memory and cognitive function. These findings suggest that atrophy of the thalamus, which has previously been associated with the generation and maintenance of focal seizures, may present at epilepsy diagnosis and relate to alterations in both white matter connectivity and cognitive performance. We suggest that at least some alterations in brain structure and consequent impact on cognitive and affective processes are the result of early epileptogenic processes rather than exclusively due to the chronicity of longstanding epilepsy, recurrent seizures, and treatment with antiseizure medication.

Effects of Gadolinium Chelate Administration Timing on T2-weighted and Diffusion-weighted Abdominal MRI Examination: A Prospective Study.

Magnetic Resonance Imaging (MRI) data acquisition includes several sequences that might be optimized to reduce the scan time. This study aimed to investigate the impact of gadolinium chelate administration timing on scan duration and image quality in Diffusion-weighted Imaging (DWI) and T2-weighted Imaging (T2WI) during abdominal MRI examinations. A prospective study was conducted from October 2018 to May 2020. Study participants were assigned into a conventional group, undergoing MRI with DWI and T2WI sequences pre and post-gadolinium injection, or an optimized group, receiving MRI with DWI and T2WI sequences after gadolinium injection. Quantitative image quality, measured by the Signal-to-noise Ratio (SNR), Contrast-to-noise Ratio (CNR), and Apparent Diffusion Coefficient (ADC), was analyzed. Kappa statistics were employed for the inter-observer agreement on liver lesion detection. Our study has included 341 patients, with 168 and 173 in the conventional and optimized groups, respectively. Mean scan durations were 1,304 (±143) and 1,015 (±129) s for the conventional and optimized groups, respectively (p<0.05). For the liver, spleen, and pancreas, SNR and ADC remained statistically unchanged in post-enhanced DWI and T2WI (p>0.05). Significant decreases in the SNR and ADC of the kidney were observed in post-contrast DWI and T2WI (p<0.05). Hepatic lesion detectability did not show significant differences between pre and post-contrast DWI and T2WI images (p>0.05). DWI and T2WI sequences assessed post-gadolinium administration exhibited shortened scan time without compromising the image quality for liver, spleen, and pancreas evaluations. However, these sequences should be examined before gadolinium administration when assessing the kidneys.

Conjugate Vertical Gaze Palsy Related to Unilateral Midbrain Infarction

Conjugate upward and downward gaze palsy related to unilateral midbrain infarction is a rare neurological symptom, as there were few reported cases worldwide. Here, we report a case of 55-year-old male patient presenting with conjugate vertical gaze palsy. In this case, diffusion-weighted magnetic resonance images demonstrated a localized infarction in the right rostral midbrain.