Apparent Diffusion Coefficient Maps Research Articles

NIMG-55. DEVELOPING A COMPUTATIONAL PIPELINE TO FORECAST AND SPATIALLY MAP HIGH-GRADE GLIOMA RESPONSE TO RADIOTHERAPY USING BIOPHYSICAL MODELS AND DATA-DRIVEN STATISTICAL ANALYSIS

Abstract High-grade glioma (HGG) presents significant treatment challenges, particularly in identifying less responsive tumor areas to target with adaptive radiotherapy (RT). This study introduces a patient-specific computational pipeline designed to predict and spatially map HGG response to RT, focusing on regions demonstrating resilience to RT. Our computational pipeline is centered around a family of 3D biophysical models describing tumor cell invasion, proliferation, and response to RT which can be personalized for individual patients using longitudinal multiparametric MRI data. Utilizing longitudinal multiparametric MRI data reporting on cell density (diffusion weighted MRI) and the extent of disease (T2-FLAIR and contrast-enhanced T1-weighted MRIs) from 21 patients, ranging from baseline to three weeks into RT, we calibrated a family of biophysical models to forecast tumor cell density up to the final week of RT and one-month follow-up. By employing pareto tail analysis on empirical cumulative distribution function fits of voxel-wise changes in apparent diffusion coefficient maps (ΔADC) between the future images (end of RT, one-month follow-up) and pre-treatment images, we identified areas of increased tumor cellularity on both the ground truth and model predicted cellularity maps. The analysis differentiated tumor voxels into “non-responding” and “responding” categories, based on cutoffs determined through ROC analysis across various conditions—including assessments of both enhancing and non-enhancing tumor regions at different time points. Optimal ΔADC cutoffs for predicted and measured data were established, leading to high area under curve (AUC; ranging from 0.803 to 0.934) values and significant Youden’s index scores (ranging from 0.526 to 0.765) across all conditions. Our findings demonstrate the feasibility of this novel pipeline in delineating biologically relevant RT targets, offering a potential tool for enhancing the efficacy of adaptive RT strategies in treating HGG.

Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas.

Apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance imaging (DWI MRI) provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images. We proposed the multiparametric residual vision transformer model (MPR-ViT) that leverages the long-range context of vision transformer (ViT) layers along with the precision of convolutional operators. Residual blocks throughout the network significantly increasing the representational power of the model. The MPR-ViT model was applied to T1w and T2-fluid attenuated inversion recovery images of 501 glioma cases from a publicly available dataset including preprocessed ADC maps. Selected patients were divided into training (N=400), validation (N=50), and test (N=51) sets, respectively. Using the preprocessed ADC maps as ground truth, model performance was evaluated and compared against the Vision Convolutional Transformer (VCT) and residual vision transformer (ResViT) models with the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). The results are as follows using T1w + T2-FLAIR MRI as inputs: MPR-ViT-PSNR: 31.0±2.1, MSE: 0.009±0.0005, SSIM: 0.950±0.015. In addition, ablation studies showed the relative impact on performance of each input sequence. Both qualitative and quantitative results indicate that the proposed MR-ViT model performs favorably against the ground truth data. We show that high-quality ADC maps can be synthesized from structural MRI using a MPR-ViT model. Our predicted images show better conformality to the ground truth volume than ResViT and VCT predictions. These high-quality synthetic ADC maps would be particularly useful for disease diagnosis and intervention, especially when ADC maps have artifacts or are unavailable.

Sub-region based histogram analysis of amide proton transfer-weighted MRI for predicting tumor budding grade in rectal adenocarcinoma: a prospective study.

To explore the sub-regional histogram features of amide proton transfer-weighted (APTw) MRI, compared with those of diffusion-weighted imaging (DWI), in predicting the tumor budding (TB) grade of rectal cancer (RC). This study prospectively enrolled 74 patients with pathologically confirmed RC, who underwent APTw MRI before surgery from July 2022 to March 2023. Hematoxylin-eosin staining was used for TB scoring. K-means clustering (K = 4-6) was applied to obtain multiple sub-regions (n = 3-5), and corresponding histogram features (including mean, standard deviation, minimum, maximum, and 10th, 25th, 50th, 75th, and 90th quantile) of APT and apparent diffusion coefficient (ADC) maps were extracted and filtered using stepwise regression. When K = 5, the K-means clustering is four sub-regions, showing the best prediction for TB grade compared to K = 4 or 6. When K = 5, there were significantly higher histogram features of the APT map in sub-regions 3 and 4 in the high TB grade group compared to the low-intermediate TB grade group. Receiver operating characteristic (ROC) curve and internal validation suggested that the predictive efficiency of the model was highest when K = 5, with AUC, sensitivity, specificity, accuracy, and kappa values of 0.92, 93%, 71%, 87%, and 0.65, respectively. There were no significant differences in the histogram features of each sub-region in the ADC map (p > 0.05). The sub-regional histogram features of APTw images can help to distinguish the heterogeneous regions of RC, which can be used to predict the TB grade of RC. Question Can the sub-regional histogram features of APTw MRI predict the tumor budding (TB) grade of rectal cancer (RC)? Findings Differences exist in histogram features of APT map subregions between high and low-intermediate TB grade groups; subregions of the APT map have different predictive abilities. Clinical relevance APT-weighted imaging might outperform DWI in predicting TB grade in RC.

Prospective Longitudinal Analysis of Physiologic MRI-based Tumor Habitat Predicts Short-term Patient Outcomes in IDH-wildtype Glioblastoma.

This study validates MRI-based tumor habitats in predicting time-to-progression (TTP), overall survival (OS), and progression site in isocitrate dehydrogenase (IDH)-wildtype glioblastoma patients. Seventy-nine patients were prospectively enrolled between January 2020 and June 2022. MRI, including diffusion-weighted and dynamic susceptibility contrast imaging, were obtained immediately post-operation and at three serial timepoints. Voxels from cerebral blood volume and apparent diffusion coefficient maps were grouped into three habitats (hypervascular cellular, hypovascular cellular, and nonviable tissue) using k-means clustering. Pre-defined cutoffs for increases in hypervascular and hypovascular cellular habitat were applied to calculate the habitat risk score. Associations between spatiotemporal habitats, habitat risk score, TTP, and OS were investigated using Cox proportional hazards modeling. Habitat risk score was compared to tumor volume using time-dependent receiver operating characteristics analysis. Progression sites were matched with spatial habitats. Increases in hypervascular and hypovascular cellular habitats and habitat risk scores were associated with shorter TTP and OS (all p < .05). Hypovascular cellular habitat and habitat risk scores 1 and 2 independently predicted TTP (hazard ratio [HR], 4.14; p=.03, HR, 4.51; p=.001 and HR, 10.02; p<.001, respectively). Hypovascular cellular habitat and habitat risk score 2 independently predicted OS (HR, 4.01, p=.003; and HR, 3.27, p<.001, respectively). Habitat risk score outperformed tumor volume in predicting TTP (12-month AUC, 0.762 vs. 0.646, p=.048). Hypovascular cellular habitat predicted progression sites (mean Dice index: 0.31). Multiparametric physiologic MRI-based spatiotemporal tumor habitats and habitat risk scores are useful biomarkers for early tumor progression and outcomes in IDH-wildtype glioblastoma patients.

Differentiation of Benign and Malignant Neck Neoplastic Lesions Using Diffusion-Weighted Magnetic Resonance Imaging.

The most difficult diagnostic challenge in neck imaging is the differentiation between benign and malignant neoplasms. The purpose of this work was to study the role of the ADC (apparent diffusion coefficient) value in discriminating benign from malignant neck neoplastic lesions. The study was conducted on 53 patients with different neck pathologies (35 malignant and 18 benign/inflammatory). In all of the subjects, conventional MRI (magnetic resonance imaging) sequences were performed apart from DWI (diffusion-weighted imaging). The mean ADC values in the benign and malignant groups were compared using the Mann-Whitney test. The ADCs of malignant lesions (mean 0.86 ± 0.28) were significantly lower than the benign lesions (mean 1.43 ± 0.57), and the mean ADC values of the inflammatory lesions (1.19 ± 0.75) were significantly lower than those of the benign lesions. The cutoff value of 1.1 mm2/s effectively differentiated benign and malignant lesions with a 97.14% sensitivity, a 77.78% specificity, and an 86.2% accuracy. There were also statistically significant differences between the ADC values of different malignant tumors of the neck (p, 0.001). NHL (0.59 ± 0.09) revealed significantly lower ADC values than SCC (0.93 ± 0.15). An ADC cutoff point of 0.7 mm2/s was the best for differentiating NHL (non-Hodgkin lymphoma) from SCC (squamous cell carcinoma); it provided a diagnostic ability of 100.0% sensitivity and 89.47% specificity. ADC mapping may be an effective MRI tool for the differentiation of benign and inflammatory lesions from malignant tumors in the neck.

The Role of Diffusion-Weighted MRI in Correlation with Contrast-Enhanced MRI and Histopathology in the Evaluation of Focal Liver Lesions.

Accurate diagnosis of focal liver lesions is of utmost importance for theinitiation ofappropriate treatment. This study aims to assess the effectiveness of diffusion-weighted imaging (DWI) in diagnosing focal liver lesions, specifically focusing on differentiating between benign and malignant lesions, distinguishing metastases from primary liver tumors, and identifying various types of both benign and malignant lesions.. The study design is that of a prospective observational study done on 28 cases withfocal liver lesions detected on ultrasound. DWI was done followed by contrast-enhanced MRI (CE-MRI). Identification of the lesion on DWI and apparent diffusion coefficient (ADC) map was madeby using the CE-MRI images as a guide. The signal intensity of the lesion on DWI is determined as either diffusion restriction or no restriction. ADC measurementswere made quantitatively over various regions of interest (ROIs) of the focal liver lesion. All the lesions were confirmed by histopathological examination (HPE) except few benign lesions like simple cysts and few hemangiomas, which were followed up by ultrasonography after six months. Our study included 28 patients with 44 focal liver lesions. Out of 28 patients in this study group (n=28), there were a total of 17 male and 11 female patients. The mean age of the sample population was 50.89±13.40 years. The lesions were more commonly seen is the age group of 41-60 years (57.14%). Out of 44 lesions(n),24 (54.5%) lesionswere benign , 20 (45.45%) were malignant. There were 17 hemangiomas (38.6%), 2 hematomas (4.5%), 2 simple cysts (4.5%), 1 hydatid cyst (2.27%), 2 hepatic adenomas (4.5%), 16 hepatocellular carcinoma (HCC) (36.36%), 3 cholangiocarcinoma (6.8%), and 1 hepatoblastoma (2.27%). DWI has 85% sensitivity and 84.7% specificity for the differentiation of benign from malignant lesions. The mean ADC value of benign lesions was 1.83x10-3mm2/s and the mean ADC of malignant liver lesions was 0.96x10-3mm2/s. In the present study, the average cut off of mean ADC to differentiate benign and malignant bone lesions is 1.3x10-3 mm2/s, with statistically significant p value of 0.001, sensitivity of 95% and specificity of 83.3%. DWI is a valuable imaging technique in the evaluation of focal liver lesions. The ability to differentiate between benign and malignant liver lesions without the need for contrast is a significant benefit, particularly in challenging cases involving uncooperative patients or when contrast administration is contraindicated.

Magnetic Resonance Imaging (MRI) of Spanish Sheep Cheese: A Study on the Relationships between Ripening Times, Geographical Origins, Textural Parameters, and MRI Parameters.

The evolution of structural changes and the textural features during the ripening process of four varieties of Spanish sheep cheese were studied using Magnetic Resonance Imaging (MRI). Specifically, longitudinal (T1) and transverse (T2) relaxation times and apparent diffusion coefficient maps were analyzed. Also, proton density was used to improve the description of the structure of the cheeses. The MRI results displayed important information about cheese matrix structure, associated with different manufacturing processes (industrial vs. traditional), ripening times (RTs, from 2 to 180 days), and geographical origins. A significant interaction between RT and cheese variety related to the variations in physicochemical and textural parameters was found. Linear regression models were developed per the abundant literature. Logarithmic regression models showed the highest R2 when monitoring the dependency on T1 and T2 parameters of water content, water activity, RT, and some texture parameters. Therefore, these results support that MRI is a useful technology to monitor the ripening process, predict textural behavior and physicochemical variables, and characterize the structure of different varieties of sheep cheese.

Correlations between Apparent Diffusion Coefficient (ADC) and Prognosis in Patients with Locally Advanced Rectal Cancer.

the aim of this study is to assess the performance of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values in predicting the response to neoadjuvant chemoradiation therapy (CRT) and outcome in patients with locally advanced rectal cancer (LARC). ninety-four patients with magnetic resonance imaging (MRI) pre- and post-neoadjuvant treatment were retrospectively enrolled. Three regions of interest (ROIs) were manually drawn on three different slices of the tumor for every DWI sequence. ROIs were positioned to include only high signal areas and avoid artifacts or necrotic areas. ROIs were automatically copied onto the corresponding ADC maps and the system derived three different ADC values, distinguishing between mean, maximum, and minimum values, and the standard deviation (SD). Only mean ADC values were considered. After surgical intervention, pTNM and the Mandard tumor regression grade (TRG) were obtained. Patients with a TRG of 1-2 were classified as responders, while patients with a TRG from 3 to 5 were classified as non-responders. no correlation was found between pre-ADC values and TRG classes, while post-ADC and ΔADC values showed a significant correlation with TRG classes (r = -0.285, p = 0.002 and r = -0.290, p = 0.019, respectively). Post-ADC values were statistically different between responders and non-responders (p = 0.019). When considering the relation between overall survival (OS) and ADC values, pre-ADC showed a negative correlation with OS (r = -0.381, p = 0.001), while a positive correlation was found between ΔADC values and OS (r = 0.323, p = 0.013). According to ΔADC values, the mean OS time between responders and non-responders showed a significant difference (p = 0.030). A statistical difference was found between TRG classes and OS (p = 0.038) and by dividing patients in responders and non-responders (p = 0.019). the pre-ADC and ΔADC values could be used as useful predictors for patient prognosis, thus helping the treatment planning. On the other hand, the post-ADC values, thanks to their relationship with the TRG classes, could be the ideal tool to predict the histopathological response and plan a conservative approach to the treatment of rectal cancer.

AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection.

Apparent Diffusion Coefficient (ADC) maps in prostate MRI can reveal tumor characteristics, but their accuracy can be compromised by artifacts related with patient motion or rectal gas associated distortions. To address these challenges, we propose a novel approach that utilizes a Generative Adversarial Network to synthesize ADC maps from T2-weighted magnetic resonance images (T2W MRI). By leveraging contrastive learning, our model accurately maps axial T2W MRI to ADC maps within the cropped region of the prostate organ boundary, capturing subtle variations and intricate structural details by learning similar and dissimilar pairs from two imaging modalities. We trained our model on a comprehensive dataset of unpaired T2-weighted images and ADC maps from 506 patients. In evaluating our model, named AI-ADC, we compared it against three state-of-the-art methods: CycleGAN, CUT, and StyTr2. Our model demonstrated a higher mean Structural Similarity Index (SSIM) of 0.863 on a test dataset of 3240 2D MRI slices from 195 patients, compared to values of 0.855, 0.797, and 0.824 for CycleGAN, CUT, and StyTr2, respectively. Similarly, our model achieved a significantly lower Fréchet Inception Distance (FID) value of 31.992, compared to values of 43.458, 179.983, and 58.784 for the other three models, indicating its superior performance in generating ADC maps. Furthermore, we evaluated our model on 147 patients from the publicly available ProstateX dataset, where it demonstrated a higher SSIM of 0.647 and a lower FID of 113.876 compared to the other three models. These results highlight the efficacy of our proposed model in generating ADC maps from T2W MRI, showcasing its potential for enhancing clinical diagnostics and radiological workflows.

Utility of diffusion-weighted imaging in differentiating benign and malignant breast lesions.

Breast cancer presents a significant global health burden. An accurate differentiation between benign and malignant lesions is imperative for timely intervention. While dynamic contrast enhanced MRI (DCE-MRI) is highly sensitive, its specificity is limited. This has led to the exploration of diffusion-weighted imaging (DWI) in distinguishing between benign and malignant breast lesions. The study aimed to explore the diagnostic utility of DWI in distinguishing between benign and malignant breast lesions. Assessment of 38 breast lesions using DWI with a b value of 800 s/mm2, performed with 3 Tesla MRI. The diagnostic performance of two different region of Interest (ROI) placement approaches was compared to obtain a feasible cut-off value of apparent diffusion coefficient (ADC) to differentiate between malignant and benign lesions. The histopathological reports were used as the gold standard. ADC values of malignant lesions were significantly lower than those of benign lesions (0.84 × 10-3 mm2/s vs. 1.54 × 10-3 mm2/s). The average ADC measured using a small-sized 2D ROI including the darkest part in the ADC map, performed better than the large 2D ROI covering the entire lesion. Using a cut-off value of 0.98 × 10-3 mm2/s, ADC obtained high sensitivity (90%) and specificity (88.9%) in distinguishing between benign and malignant breast lesions. Utilising quantitative analysis of DWI with ADC value measurement, reliably distinguished between benign and malignant breast lesions in this cohort, especially when employing a higher b value of 800 s/mm2.

Radiomic features on multiparametric MRI for differentiating pseudoprogression from recurrence in high-grade gliomas.

Distinguishing between tumor recurrence and pseudoprogression (PsP) in high-grade glioma postoperatively is challenging. This study aims to enhance this differentiation using a combination of intratumoral and peritumoral radiomics. To assess the effectiveness of intratumoral and peritumoral radiomics in improving the differentiation between high-grade glioma recurrence and pseudoprogression after surgery. A total of 109 cases were randomly divided into training and validation sets, with 1316 features extracted from intratumoral and peritumoral volumes of interest (VOIs) on conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps. Feature selection was performed using the mRMR algorithm, resulting in intratumoral (100 features), peritumoral (100 features), and combined (200 features) subsets. Optimal features were then selected using PCC and RFE algorithms and modeled using LR, SVM, and LDA classifiers. Diagnostic performance was compared using area under the receiver operating characteristic curve (AUC), evaluated in the validation set. A nomogram was established using radscores from intratumoral, peritumoral, and combined models. The combined model, utilizing 14 optimal features (8 peritumoral, 6 intratumoral) and LR as the best classifier, outperformed the single intratumoral and peritumoral models. In the training set, the AUC values for the combined model, intratumoral model, and peritumoral model were 0.938, 0.921, and 0.847, respectively; in the validation set, the AUC values were 0.841, 0.755, and 0.705. The nomogram model demonstrated AUCs of 0.960 (training set) and 0.850 (validation set). The combination of intratumoral and peritumoral radiomics is effective in distinguishing high-grade glioma recurrence from pseudoprogression after surgery.

Vendor-Specific Correction Software for Apparent Diffusion Coefficient Bias Due to Gradient Nonlinearity in Breast Diffusion-Weighted Imaging Using Ice-Water Phantom.

This study aimed to evaluate a vendor-specific correction software for apparent diffusion coefficient (ADC) bias due to gradient nonlinearity in breast diffusion-weighted magnetic resonance imaging using an ice-water phantom. The phantom consists of 5 plastic tubes with a length of 100 mm and a diameter of 15 mm, filled with distilled water and immersed in an ice-water bath. Diffusion-weighted images were acquired by echo-planar imaging sequence on a 3.0-T scanner. ADC maps with and without correction were calculated using 4 b -values (0, 100, 600, and 800 s/mm 2 ). The mean ADCs were measured using a rectangular profile with 5 × 40 pixels in the anterior-posterior (AP) and a square region of interest with 5 × 5 pixels in the right-left (RL) and superior-inferior (SI) directions on the ADC map. ADC was compared with and without correction using a paired t test. Additionally, ADC of the ice-water phantom was measured at the magnet isocenter. ADC increased in the AP and RL directions and decreased in the SI direction with increasing distance from the isocenter before correction. After the correction, ADC at the off-center positions in the AP, RL, and SI directions was reduced to within 5% of the expected value. There were significant differences in the ADC at the off-center positions without and with correction ( P < 0.001); however, ADC at the magnet isocenter did not vary after correction (1.08 ± 0.02 × 10 -3 mm 2 /s). The vendor-specific software corrected the ADC bias due to gradient nonlinearity at the off-center positions in the AP, RL, and SI directions. Therefore, the software will contribute to the accurate ADC assessment in breast DWI.

Diffusion-weighted MRI in characterization of enlarged cervical lymph nodes

Background Diffusion-weighted MRI (DWI) has been shown to enhance the visibility of sub-centimetric lymph nodes (LNs) by suppressing background tissue, thereby facilitating LN identification in comparison to conventional sequences. Aim The purpose of this research is to evaluate the efficacy of DWI and apparent diffusion coefficient in distinguishing among cancerous and benign cervical lymphadenopathy. Patients and methods This prospective research includes 50 cases complaining of cervical LN swelling referred to MR units of Radiodiagnosis and Clinical Oncology department at Tanta University Hospital, with symptoms of enlarged cervical LN. Results The most frequently encountered lesion was lymphoma in 12 (24.00%) cases, followed by metastatic from thyroid carcinoma in 7 (14%) cases and metastasis from cancer parotid in 6 (12%) cases, 5 (10%) cases were diagnosed as metastasis from tongue, metastasis from oropharyngeal mass was found in 4 (8%) cases, 3 cases were diagnosed as TB and acute inflammatory with percentage of (6%) for each one, where there were 2 cases diagnosed with percentage of (4%). This study revealed that DWI is a technique for investigation of tumors histological content of LNs and used for distinguishing among benign and cancerous ones. Conclusion DWI with apparent diffusion coefficient map is a novel and promising method for distinguishing cancerous from noncancerous LN. The addition of DWI to standard MRI gives extra biological and functional information for characterizing cervical LN.

Diffusion-Weighted MRI for Intracranial Tumor Differentiation: Analyzing the Role of Apparent Diffusion Coefficient Values

Magnetic Resonance Imaging (MRI) has become a standard non-invasive method for evaluating brain lesions, offering highly detailed structural data. However, traditional MRI faces limitations in differentiating between various tumor components, peritumoral edema, and healthy brain tissue. Diffusion-Weighted Imaging (DWI), an advanced MRI modality, overcomes some of these challenges by measuring water molecule movement within tissues, providing additional insights for precise tumor evaluation. This study explores the diffusion characteristics of intracranial tumor components using DWI. Forty patients with intracranial tumors underwent both conventional MRI and DWI, utilizing an echo-planar diffusion imaging protocol to create apparent diffusion coefficient (ADC) maps. Absolute ADC values were calculated for tumor components, peritumoral edema, and normal brain tissue. Meningiomas emerged as the primary extra-axial lesions with higher ADC values than those of normal tissue, while gliomas—primarily astrocytomas—constituted the main intra-axial lesions with mean ADC values of 1.2 ± 0.69 x 10⁻³ mm²/s. This study concludes that DWI significantly enhances the diagnosis and characterization of intracranial neoplasms, improving differentiation between tumor components and surrounding tissues.

Characterization of Classic Meningioma with Use of Conventional Magnetic Resonance and Diffusion Tensor Imaging

Introduction: The conventional magnetic resonance imaging (MRI) method is widely considered ‘with limited success’ in differentiating the meningioma types but may fail to localize the tumor occupation of white-matter fiber bundles accurately. Diffusion tensor imaging (DTI) is considered as an imaging modality that may elucidate the microstructure of brain tumors. We provide characteristics of meningioma using DTI-based-three-dimensional tracing of white matter to portray meningioma in a noninvasive approach and its structural contact to contiguous tumors and elucidate the influence of occupying lesions on white-matter fiber bundles. Case Presentation: A 28-year-old female presented with visuospatial disturbances and persistent headaches for 2 years. Conventional and advanced MRI studies were performed. Diffusion-weighted Images (DWI) and Apparent Diffusion Coefficient (ADC) values were measured in the lesion using routine MRI sequences. Advanced MRI using DTI was also performed. Conventional MRI outcomes showed tumor parenchyma, peritumoral edema, and compression on the circumnavigated brain tissue. There was hyperintense on DW trace image and isointense on ADC map. On T2-weighted image (T2WI) and Fluid-Attenuated Inversion Recovery (FLAIR) images, there was an increased signal intensity that demonstrated an extra-axial lesion, while T1-weighted imaging signals showed hypointensity. DTI fractional anisotropy (FA) marker is an unclear optic radiation in the concerned area, indicating the shift or destruction of the optic radiation. The mean FA values of solid-enhancing areas of meningioma were 0.28 ± 0.17. Mean ADC values (103 mm2/s) were 0.764 ± 0.172. Conclusions: Classic meningioma in this case has low intratumoral FA and high ADC. DTI displayed that intratumoral microscopic water motion is disorganized.

The Role of Perfusion Magnetic Resonance Imaging in the Discrimination of Benign vs Malign Character of Musculoskeletal Tumors

Purpose: There may be difficulties in diagnosing musculoskeletal system tumors with Magnetic Resonance Imaging (MRI). Advanced MRI imaging techniques may contribute to noninvasive diagnosis.The aim of our study was to evaluate the performance of perfusion magnetic resonance imaging quantitative data in the differentiation of benign and malignant musculoskeletal tumors. Material and Methods: Thirty-six histologically proven patients were included in the study retrospectively. All patients underwent 1.5-T perfusion MRI (magnetic resonance imaging) with T1 mapping and diffusion MRI. Four quantitative and one semiquantitative parameter were obtained for each tumor using the Toft pharmacokinetic model. ADCmean and ADCmin values were calculated from apparent diffusion coefficient (ADC) maps. Results: Eighteen of 36 patients (50%) had malignant tumors, and 18 had benign tumors. There were 26 soft tissue tumors and 10 bone tissue tumors. Among malignant and benign tumors, the best performance was found in the Ktrans, Kep, Ve values. (p=0.011, p=0.013, p=0,035). Conclusion: Ktrans and Kep values, which are indicators of increased permeability in the tumor, are noninvasive parameters for determining the malignant character of the tumor. The 'Ve' value is an indicator of the extracellular volume of the tumor. Ktrans, Kep and Ve maps can also guide the biopsy.

Woodhouse-sakati syndrome with no reportable MRI findings: a case report

BackgroundWoodhouse-Sakati Syndrome (WSS) is a rare autosomal recessive condition caused by biallelic pathogenic variants in the DCAF17 gene, with fewer than 200 cases reported in the literature. Symptoms first emerge in middle-late adolescence with a spectrum of hypogonadal and progressive neurological features.Case presentationWe present a case of WSS with no reportable T2-weighted, apparent diffusion coefficient mapping and susceptibility weighted MRI findings. This differs from cases reported in the current literature. Our patient developed abnormal movements in both legs, clumsiness of the hands, dysarthria, and swallowing difficulties. Moreover, she presented with alopecia manifesting as frontal and temporal balding, severe dystonia with painful dystonic spasms primarily in the left upper limb, as well as primary amenorrhea. She was not independently ambulatory on presentation, requiring wheelchair assistance. Genetic testing, the crucial test for a definitive diagnosis, was undertaken in Qatar and confirmed WSS. Treatment provided includes botulinum toxin injections and deep brain stimulation, providing better dystonia control, with progress in walking and strength exercises, and overall remarkable improvement. Intensive neurorehabilitation regimes were also deployed from admission, including physiotherapy, occupational therapy and speech and language therapy.ConclusionThis case adds to the current literature on WSS manifestations, with all previously reported cases having positive MRI findings, unlike our case.

Characteristics and diagnostic performance of nonechoplanar diffusion-weighted imaging in detecting canine tympanokeratoma (cholesteatoma).

Tympanokeratomas (cholesteatomas) are destructive keratinic masses of the middle ear that are often treated surgically. In humans, nonechoplanar (non-EP) diffusion-weighted (DW) MRI is efficient in diagnosing tympanokeratomas and differentiating recurrent tympanokeratomas from granulation or fibrous tissue after surgery. The objectives of this study were to (1) determine the characteristics of non-EP DWI of histologically or cytologically confirmed canine tympanokeratomas, (2) determine the performance of non-EP DW images, apparent diffusion coefficient (ADC) map and ADC values in discriminating between tympanokeratomas and other causes of otitis media in dogs, and (3) find an optimal ADC cut-off value. Medical records of dogs diagnosed on MRI with otitis media in two veterinary hospitals were retrospectively reviewed. Sixty-two ears with a cytological or pathological diagnosis of either tympanokeratoma (36/62) or nontympanokeratoma otitis media (26/62) were selected. Diffusion-weighted images had an accuracy of 77.4%, a sensitivity of 94.4%, and a specificity of 53.8% for the detection of tympanokeratoma. The ADC value of middle ear content ranged from 517 to 1355.10-6mm2/s in tympanokeratoma and from 454 to 2447×10-6mm2/s in nontympanokeratoma otitis media. An ADC value below 916×10-6mm2/s interpreted as a tympanokeratoma yielded an accuracy of 78.7%, a sensitivity of 92.6%, and a specificity of 60%. In the study population, combined qualitative analysis of DW images and ADC maps was the most effective method for diagnosing tympanokeratomas with an accuracy of 80.9%, a sensitivity of 92.6%, and a specificity of 65%.

Fotemustine in recurrent high‑grade glioma: MRI neuro‑radiological findings.

The use of fotemustine (FTM) has been authorized in certain countries for the treatment of recurrent high-grade gliomas (HGG) after Stupp therapy. However, to the best of our knowledge, no studies have assessed changes in magnetic resonance imaging (MRI) during treatment with FTM monotherapy. The aim of the present study was to assess the neuroradiological findings in a cohort of patients with recurrent HGG treated with FTM monotherapy. Patients with HGG already undergoing the Stupp protocol were retrospectively included. MRIs (pre- and post-FTM treatment) were analyzed by two neuroradiologists in consensus: Volume and diffusion values of the contrast-enhanced component were measured on T1-weighted volumetric sequences after gadolinium injection and on apparent diffusion coefficient (ADC) maps, respectively. A total of 19 patients [median age, 49 years; interquartile range (IQR), 43-57 years] were included, 17 of whom had glioblastoma and 2 had astrocytoma isocitrate dehydrogenase-mutated grade 4. The median duration of FTM therapy was 4 months (IQR, 2-6 months). The median tumor volume measured on the contrast-enhanced component was 2,216 mm3 (IQR, 768-13,169 mm3) at baseline and 9,217 mm3 (IQR, 3,455-16,697 mm3) at the end of treatment, with a median change of +38% (IQR, -45-+574%). A total of seven patients showed a volume decrease. ADC value analysis of the enhancement area demonstrated no significant difference between the pre- and the post-FTM treatment periods (P=0.36); however, in three patients, the decreases in ADC levels were particularly marked. In conclusion, the present study described a series of patients with recurrent HGG treated with FTM in monotherapy, demonstrating a prevalent increase in lesion enhancement and three cases of marked restrictions on diffusion-weighted imaging. Further prospective studies are required to corroborate such preliminary results.

Bacillus Calmette-Guérin Instillations May Mimic Prostate Cancer on Multiparametric Magnetic Resonance Imaging.

Granulomatous prostatitis (GP) is a rare and benign inflammatory condition of the prostate, often mimicking prostate cancer (PCa) in clinical and radiological evaluations. This study examines the characteristics and diagnostic challenges of GP in a cohort of 12 patients who received Bacillus Calmette-Guérin (BCG) therapy following treatment for non-muscle-invasive bladder urothelial carcinoma. In this case series, we analyze their clinical presentations, MRI findings, and histopathological results. Patients presented with elevated PSA levels and firm or nodular prostates on digital rectal examination, complicating the differentiation from PCa. Multiparametric MRI showed lesions with hypointensity on T2-weighted images, hypersignal on diffusion-weighted imaging and hyposignal on the apparent diffusion coefficient map, further mimicking malignancy. Histopathological examination remains the gold standard for diagnosing GP, distinguishing it from PCa through the identification of granulomas and associated inflammatory cells. This study underscores the importance of awareness and accurate diagnosis of GP to avoid unnecessary biopsies and treatments, highlighting the need for a multidisciplinary approach combining clinical, imaging, and pathological data.