Search Results (109)

Background/Objectives: Accurate segmentation of brain tumors in Magnetic Resonance Imaging (MRI) scans is critical for diagnosis and treatment planning due to their life-threatening nature. This study aims to develop a robust and automated method capable of precisely delineating heterogeneous tumor regions while improving segmentation accuracy and generalization. Methods: We propose Attention Res-UNet (ARU-Net), a novel Deep Learning (DL) architecture integrating residual connections, Adaptive Channel Attention (ACA), and Dimensional-space Triplet Attention (DTA) modules. The encoding module efficiently extracts and refines relevant feature information by applying ACA to the lower layers of convolutional and residual blocks. The DTA is fixed to the upper layers of the decoding module, decoupling channel weights to better extract and fuse multi-scale features, enhancing both performance and efficiency. Input MRI images are pre-processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, denoising filters, and Linear Kuwahara filtering to preserve edges while smoothing homogeneous regions. The network is trained using categorical cross-entropy loss with the Adam optimizer on the BTMRII dataset, and comparative experiments are conducted against baseline U-Net, DenseNet121, and Xception models. Performance is evaluated using accuracy, precision, recall, F1-score, Dice Similarity Coefficient (DSC), and Intersection over Union (IoU) metrics. Results: Baseline U-Net showed significant performance gains after adding residual connections and ACA modules, with DSC improving by approximately 3.3%, accuracy by 3.2%, IoU by 7.7%, and F1-score by 3.3%. ARU-Net further enhanced segmentation performance, achieving 98.3% accuracy, 98.1% DSC, 96.3% IoU, and a superior F1-score, representing additional improvements of 1.1–2.0% over the U-Net + Residual + ACA variant. Visualizations confirmed smoother boundaries and more precise tumor contours across all six tumor classes, highlighting ARU-Net’s ability to capture heterogeneous tumor structures and fine structural details more effectively than both baseline U-Net and other conventional DL models. Conclusions: ARU-Net, combined with an effective pre-processing strategy, provides a highly reliable and precise solution for automated brain tumor segmentation. Its improvements across multiple evaluation metrics over U-Net and other conventional models highlight its potential for clinical application and contribute novel insights to medical image analysis research. Full article

Background/Objectives: This study aimed to evaluate the diagnostic performance of simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters in distinguishing malignant from benign breast lesions, and to explore their association with clinicopathological features. Methods: This retrospective study included 108 women who underwent breast MRI with multi-b-value DWI (0, 20, 200, 500, 800 s/mm²). Of those 108 women, 73 had pathologically confirmed malignant lesions. IVIM maps (ADC_map, D, D*, and perfusion fraction f) were generated using IB-Diffusion™ software version 21.12. Lesions were manually segmented by radiologists, and clinicopathological data including receptor status, Ki-67 index, cancer type, histologic grade, and molecular subtype were extracted from medical records. Nonparametric tests and ROC analysis were used to assess group differences and diagnostic performance. Additionally, a binary logistic regression model combining D, D*, and f was developed to evaluate their joint diagnostic utility, with ROC analysis applied to the model’s predicted probabilities. Results: Malignant lesions demonstrated significantly lower diffusion parameters compared to benign lesions, including ADC_map (p = 0.004), D (p = 0.009), and D* (p = 0.016), indicating restricted diffusion in cancerous tissue. In contrast, the perfusion fraction (f) did not show a significant difference (p = 0.202). ROC analysis revealed moderate diagnostic accuracy for ADC_map (AUC = 0.671), D (AUC = 0.657), and D* (AUC = 0.644), while f showed poor discrimination (AUC = 0.576, p = 0.186). A combined logistic regression model using D, D*, and f significantly improved diagnostic performance, achieving an AUC of 0.725 (p < 0.001), with 67.1% sensitivity and 74.3% specificity. ADC_map achieved the highest sensitivity (100%) but had low specificity (11.4%). Among clinicopathological features, only histologic grade was significantly associated with IVIM metrics, with higher-grade tumors showing lower ADC_map and D* values (p = 0.042 and p = 0.046, respectively). No significant associations were found between IVIM parameters and ER, PR, HER2 status, Ki-67 index, cancer type, or molecular subtype. Conclusions: Simplified IVIM DWI offers moderate accuracy in distinguishing malignant from benign breast lesions, with diffusion-related parameters (ADC_map, D, D*) showing the strongest diagnostic value. Incorporating D, D*, and f into a combined model enhanced diagnostic performance compared to individual IVIM metrics, supporting the potential of multivariate IVIM analysis in breast lesion characterization. Tumor grade was the only clinicopathological feature consistently associated with diffusion metrics, suggesting that IVIM may reflect underlying tumor differentiation but has limited utility for molecular subtype classification. Full article

Background/Objectives: Postoperative MRI is crucial for detecting residual tumor, identifying complications, and planning subsequent therapy. This study evaluates accelerated deep learning reconstruction (DLR) versus standard clinical protocols for early postoperative MRI following tumor resection. Methods: This study uses a multidisciplinary approach involving a neuroradiologist, neurosurgeon, neuro-oncologist, and radiotherapist to evaluate qualitative aspects using a 5-point Likert scale, the preferred reconstruction variant and potential residual tumor of DLR and conventional reconstruction (CR) of FLAIR, T1-weighted non-contrast and contrast-enhanced (T1), and coronal T2-weighted (T2) sequences for 1.5 and 3 T MRI. Quantitative analysis included the image quality metrics Structural Similarity Index (SSIM), Multi-Scale SSIM (MS-SSIM), Feature Similarity Index (FSIM), Noise Quality Metric (NQM), signal-to-noise ratio (SNR), and Peak SNR (PSNR) with CR as a reference. Results: All raters strongly preferred DLR over CR. This was most pronounced for FLAIR images at 1.5 and 3 T (91% at 1.5 T and 97% at 3 T) and least pronounced for T1 at 1.5 T (79% for non-contrast-enhanced and 84% for contrast-enhanced sequences) and for T2 at 3 T (69%). DLR demonstrated superior qualitative image quality for all sequences and field strengths (p < 0.001), except for T2 at 3 T, which was observed across all raters (p = 0.670). Diagnostic confidence was similar at 3 T with better but non-significant differences for T2 (p = 0.134) and at 1.5 T with better but non-significant differences for non-contrast-enhanced T1 (p = 0.083) and only marginally significant results for FLAIR (p = 0.033). Both the SSIM and MS-SSIM indicated near-perfect similarity between CR and DLR. FSIM performs worse in terms of consistency between CR and DLR. The image quality metrics NQM, SNR, and PSNR showed better results for DLR. Visual assessment of residual tumor was similar at 3 T but differed at 1.5 T, with more residual tumor detected with DLR, especially by the neurosurgeon (n = 4). Conclusions: An accelerated DLR protocol demonstrates clinical feasibility, enabling high-quality reconstructions in challenging postoperative MRIs. DLR sequences received strong multidisciplinary preference, underscoring their potential to improve neuro-oncologic decision making and suitability for clinical implementation. Full article

Background/Objectives: Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents; the survival rate is as low as 24%. Accurate prediction of clinical outcomes remains a challenge due to tumor heterogeneity and the complexity of pediatric cases. This study aims to improve predictions of progressive disease, therapy response, relapse, and survival in pediatric OS using MRI-based radiomics and machine learning methods. Methods: Pre-treatment contrast-enhanced coronal T1-weighted MR scans were collected from 63 pediatric OS patients, with an additional nine external cases used for validation. Three strategies were considered for target region segmentation (whole-tumor, tumor sampling, and bone/soft tissue) and used for MRI-based radiomics. These were then combined with clinical features to predict OS clinical outcomes. Results: The mean age of OS patients was 11.8 ± 3.5 years. Most tumors were located in the femur (65%). Osteoblastic subtype was the most common histological classification (79%). The majority of OS patients (79%) did not have evidence of metastasis at diagnosis. Progressive disease occurred in 27% of patients, 59% of patients showed adequate therapy response, 25% experienced relapse after therapy, and 30% died from OS. Classification models based on bone/soft tissue segmentation generally performed the best, with certain clinical features improving performance, especially for therapy response and mortality. The top performing classifier in each outcome achieved 0.94–1.0 validation ROC AUC and 0.63–1.0 testing ROC AUC, while those without radiomic features (RFs) generally performed suboptimally. Conclusions: This study demonstrates the strong predictive capabilities of MRI-based radiomics and multi-region segmentations for predicting clinical outcomes in pediatric OS. Full article

Background: Traumatic brain injury (TBI) remains a significant contributor to global mortality and long-term neurological disability. Accurate prognostic biomarkers are crucial for enhancing prognostic accuracy and guiding personalized clinical management. Objective: This review assesses the prognostic value of arterial spin labeling (ASL), a non-invasive MRI technique, in adult and pediatric TBI, with a focus on quantitative cerebral blood flow (CBF) and arterial transit time (ATT) measures. A comprehensive literature search was conducted across PubMed, Embase, Scopus, and IEEE databases, including observational studies and clinical trials that applied ASL techniques (pCASL, PASL, VSASL, multi-PLD) in TBI patients with functional or cognitive outcomes, with outcome assessments conducted at least 3 months post-injury. Results: ASL-derived CBF and ATT parameters demonstrate potential as prognostic indicators across both acute and chronic stages of TBI. Hypoperfusion patterns correlate with worse neurocognitive outcomes, while region-specific perfusion alterations are associated with affective symptoms. Multi-delay and velocity-selective ASL sequences enhance diagnostic sensitivity in TBI with heterogeneous perfusion dynamics. Compared to conventional perfusion imaging, ASL provides absolute quantification without contrast agents, making it suitable for repeated monitoring in vulnerable populations. ASL emerges as a promising prognostic biomarker for clinical use in TBI. Conclusion: Integrating ASL into multiparametric models may improve risk stratification and guide individualized therapeutic strategies. Full article

Background/Objectives: This study aimed to develop a reliable scoring system combining clinical and radiological parameters to distinguish atypical lipomatous tumours (ALTs) from lipomas, improving diagnostic accuracy and reducing expensive molecular pathology testing. Methods: A retrospective analysis of 188 patients who underwent surgery for lipomatous tumours was conducted. Patient data, including medical history, pathology, and MRI imaging results, were reviewed. Four predictive models were developed using various clinical and imaging parameters, including age, tumour size, location, and MRI characteristics (homogeneity, contrast enhancement). Statistical analysis, including ROC curve analysis and logistic regression, was performed to assess the accuracy of these models. Results: The highest predictive accuracy was achieved with Model 1, which included seven parameters, yielding an AUC of 0.952. This model achieved a sensitivity of 96.4% and a negative predictive value (NPV) of 97.2%. Reducing the number of parameters lowered the accuracy, with contrast enhancement playing a significant role in Model 1. A risk calculator based on the optimal model was developed, offering an effective tool for clinical use that can be provided. Notably, 21 out of 37 ALTs lacked atypia and would have been missed without molecular testing. Conclusions: The developed scoring system, based on clinical and imaging parameters, accurately distinguishes ALTs from lipomas, offering a practical alternative to molecular pathology testing. This multi-parameter approach significantly improves diagnostic reliability, reducing the risk of misclassification and false negatives, while also potentially lowering healthcare costs. Full article

Background/Objectives: Fabry disease (FD) is a lysosomal storage disorder often associated with early-onset neuropathic pain, attributed to small fibre neuropathy (SFN). The dorsal root ganglion (DRG) has emerged as a critical site of early pathophysiological involvement in FD, with structural and functional alterations implicated in the development of neuropathic symptoms. This exploratory study introduces DRG proton density (DRG-PD) as a novel MRI-derived biomarker and evaluates its association with SFN. Methods: Eighty genetically confirmed FD patients underwent high-resolution 3T MRI with DRG-PD quantification at the lumbosacral levels L5 and S1. DRG-PD was derived from B1-corrected multi-echo spin echo sequences and normalised to cerebrospinal fluid intensity. All patients underwent clinical, biochemical and histological evaluation to determine SFN status. Associations between DRG imaging parameters and clinical variables were analysed using correlation and regression models. Diagnostic performance was evaluated using receiver operating characteristic curve analysis. Results: DRG-PD values were significantly increased in patients with classical FD and SFN, demonstrating a large effect size (Cliff’s δ = 0.92) and excellent discriminatory performance (AUC = 0.96). In contrast, DRG volume and T2 relaxation time were not significantly associated with SFN status. DRG-PD remained an independent predictor of SFN in multivariable logistic regression (p = 0.019). Conclusions: DRG-PD is a non-invasive correlate of SFN in classical FD. It may provide superior diagnostic value compared to existing MRI metrics and reflects proximal ganglionic pathology not captured by distal histological assessments. Full article

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article

Abdominal organ segmentation in CT images is crucial for accurate diagnosis, treatment planning, and condition monitoring. However, the annotation process is often hindered by challenges such as low contrast, artifacts, and complex organ structures. While unsupervised domain adaptation (UDA) has shown promise in addressing these issues by transferring knowledge from a different modality (source domain), its reliance on both source and target data during training presents a practical challenge in many clinical settings due to data privacy concerns. This study aims to develop a cross-modality abdominal multi-organ segmentation model for label-free CT (target domain) data, leveraging knowledge solely from a pre-trained source domain (MRI) model without accessing the source data. To achieve this, we generate source-like images from target-domain images using a one-way image translation approach with the pre-trained model. These synthesized images preserve the anatomical structure of the target, enabling segmentation predictions from the pre-trained model. To further enhance segmentation accuracy, particularly for organ boundaries and small contours, we introduce an auxiliary translation module with an image decoder and multi-level discriminator. The results demonstrate significant improvements across several performance metrics, including the Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD), highlighting the effectiveness of the proposed method. Full article

Accurate simulation of extreme precipitation events is crucial for managing climate-vulnerable sectors in Southern Africa, as such events directly impact agriculture, water resources, and disaster preparedness. However, global climate models frequently struggle to capture these phenomena, which limits their practical applicability. This study investigates the effectiveness of three bias correction techniques—scaled distribution mapping (SDM), quantile distribution mapping (QDM), and QDM with a focus on precipitation above and below the 95th percentile (QDM95)—and the daily precipitation outputs from 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset was served as a reference. The bias-corrected and native models were evaluated against three observational datasets—the CHIRPS, Multi-Source Weighted Ensemble Precipitation (MSWEP), and Global Precipitation Climatology Center (GPCC) datasets—for the period of 1982–2014, focusing on the December-January-February season. The ability of the models to generate eight extreme precipitation indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated. The results show that the native and bias-corrected models captured similar spatial patterns of extreme precipitation, but there were significant changes in the amount of extreme precipitation episodes. While bias correction generally improved the spatial representation of extreme precipitation, its effectiveness varied depending on the reference dataset used, particularly for the maximum one-day precipitation (Rx1day), consecutive wet days (CWD), consecutive dry days (CDD), extremely wet days (R95p), and simple daily intensity index (SDII). In contrast, the total rain days (RR1), heavy precipitation days (R10mm), and extremely heavy precipitation days (R20mm) showed consistent improvement across all observations. All three bias correction techniques enhanced the accuracy of the models across all extreme indices, as demonstrated by higher pattern correlation coefficients, improved Taylor skill scores (TSSs), reduced root mean square errors, and fewer biases. The ranking of models using the comprehensive rating index (CRI) indicates that no single model consistently outperformed the others across all bias-corrected techniques relative to the CHIRPS, GPCC, and MSWEP datasets. Among the three bias correction methods, SDM and QDM95 outperformed QDM for a variety of criteria. Among the bias-corrected strategies, the best-performing models were EC-Earth3-Veg, EC-Earth3, MRI-ESM2, and the multi-model ensemble (MME). These findings demonstrate the efficiency of bias correction in improving the modeling of precipitation extremes in Southern Africa, ultimately boosting climate impact assessments. Full article

Goal: This study evaluates the feasibility and outcome of a personalized MRI-based liver SBRT treatment planning platform with the SPION contrast agent Ferumoxytol^® (Sandoz Inc.; Princeton, NJ, USA) to maintain a superior real-time visualization of liver tumors and volumes of functional hepatic parenchyma for radiotherapy planning throughout multi-fractionated liver SBRT with online plan adaptations on an Elekta Unity 1.5 T MR-Linac (Elekta; Stockholm, Sweden). Materials and Methods: Patients underwent SPION-enhanced MRI on the Elekta Unity MR-Linac for improved tumor and functional hepatic parenchyma visualization. An automated contouring algorithm was applied for the delineation and subsequent guided avoidance of functional liver parenchyma volumes (FLVs) on the SPION-enhanced MR-Linac. Radiation dose constraints were adapted exclusively to FLV. Local control, toxicity, and survival were assessed with at least 6-month radiographic follow-up. Pre- and post-transplant outcomes were analyzed in the subset of patients with HCC and hepatic cirrhosis who completed SBRT as a bridge to liver transplant. Model of End-Stage Liver Disease (MELD-Na) was used to score hepatic function before and after SBRT. Results: With a median follow-up of 23 months (range: 3–40 months), 23 HCC patients (26 lesions treated) and 9 patients (14 lesions treated) with hepatic metastases received SBRT (mean dose: 48 Gy, range: 36–54 Gy) in 1–5 fractions. Nearly all patients in this study had pe-existing liver conditions, including hepatic cirrhosis (23), prior TACE (7), prior SBRT (18), or history of hepatic resection (2). Compared to the non-contrast images, SPIONs improved tumor visibility on post-SPION images on the background of negatively enhancing functionally active hepatic parenchyma. Prolonged SPION-contrast retention within hepatic parenchyma enabled per-fraction treatment adaptation throughout the entire multi-fraction treatment course. FLV loss (53%, p < 0.0001) was observed in cirrhotic patients, but functional and anatomic liver volumes remained consistent in non-cirrhotic patients. Mean dose to FLV was maintained within the liver threshold tolerance to radiation in all patients after the optimization of Step-and-Shoot Intensity-Modulated Radiotherapy (SS-IMRT) on the SPION-enhanced MRI-Linac. No radiation-induced liver disease was observed within 6 months post-SBRT, and the MELD-Na score in cirrhotic patients was not significantly elevated at 3-month intervals after SBRT completion. Conclusions: SPION Ferumoxytol^® administered intravenously as an alternative MRI contrast agent on the day of SBRT planning produces a long-lasting contrast effect between tumors and functional hepatic parenchyma for precision targeting and guided avoidance during the entire course of liver SBRT, enabling fast and accurate online plan adaptation on the 1.5 T Elekta Unity MR-Linac. This approach demonstrates a safe and effective bridging therapy for patients with hepatic cirrhosis, leading to low toxicity and favorable transplant outcomes. Full article

Accurate detection and diagnosis of brain tumors at early stages is significant for effective treatment. While numerous methods have been developed for tumor detection and classification, several rely on traditional techniques, often resulting in suboptimal performance. In contrast, AI-based deep learning techniques have shown promising results, consistently achieving high accuracy across various tumor types while maintaining model interpretability. Inspired by these advancements, this paper introduces an improved variant of EfficientNet for multi-grade brain tumor detection and classification, addressing the gap between performance and explainability. Our approach extends the capabilities of EfficientNet to classify four tumor types: glioma, meningioma, pituitary tumor, and non-tumor. For enhanced explainability, we incorporate gradient-weighted class activation mapping (Grad-CAM) to improve model interpretability. The input MRI images undergo data augmentation before being passed through the feature extraction phase, where the underlying tumor patterns are learned. Our model achieves an average accuracy of 98.6%, surpassing other state-of-the-art methods on standard datasets while maintaining a substantially reduced parameter count. Furthermore, the explainable AI (XAI) analysis demonstrates the model’s ability to focus on relevant tumor regions, enhancing its interpretability. This accurate and interpretable model for brain tumor classification has the potential to significantly aid clinical decision-making in neuro-oncology. Full article

Background/Objectives: Intraneural tumors (INTs) pose a diagnostic challenge, owing to their varied origins within nerve fascicles and their wide spectrum, which includes both benign and malignant forms. Accurate diagnosis and management of these tumors depends upon the skills of the radiologist in identifying key imaging features and correlating them with the patient’s clinical symptoms and examination findings. Methods: This comprehensive review systematically analyzes the various imaging features in the diagnosis of intraneural tumors, ranging from basic MR to advanced MR imaging techniques such as MR neurography (MRN), diffusion tensor imaging (DTI), and dynamic contrast-enhanced (DCE) MRI. Results: The article emphasizes the differentiation of benign from malignant lesions using characteristic MRI features, such as the “target sign” and “split-fat sign” for tumor characterization. The role of advanced multiparametric MRI in improving biopsy planning, guiding surgical mapping, and enhancing post-treatment monitoring is also highlighted. The review also underlines the importance of common diagnostic pitfalls and highlights the need for a multi-disciplinary approach to achieve an accurate diagnosis, appropriate treatment strategy, and post-therapy surveillance planning. Conclusions: In this review, we illustrate the main imaging findings of intraneural tumors, focusing on specific MR imaging features that are crucial for an accurate diagnosis and the differentiation between benign and malignant lesions. Full article

Neuroradiology, as a modern branch of the neurosciences and radiological sciences, has an impact on global health, particularly on brain health. On the one hand, neuroradiology directly impacts diseases of the nervous system, such as stroke and inflammatory diseases, by providing an all-in-one package combining imaging, diagnosis, treatment, and follow-up. This has been impacted by the continuous evolution over the last decades of both diagnostic and interventional tools in parallel: this was the case in stroke, where the endovascular treatment was followed closely by developments in fast MRI techniques and multi-slice CT imaging. Additionally, inflammatory diseases of the brain, as well as tumors of the central nervous system, can be imaged and localized in order to set in place both an early diagnosis and initiate treatment. Neurodegenerative diseases such as Alzheimer’s disease, in which treatment options are appearing on the horizon, also benefit from the use of modern neuroimaging techniques. On the other hand, neuroradiology plays an important role in the prevention and prediction of brain diseases and helps in building up the so-called digital twin, often from birth till late in life. Additionally, the practice of neuroradiology itself is evolving to not only improve patient health but also the health of the practitioners of neuroradiology themselves. By improving the overall work environment also, neuroradiologists will be working under better conditions and will suffer less fatigue and burn-out, thereby providing better service to patients and population. By using less radiation for diagnostic tests and shifting to techniques that rely more and more on either magnetic resonance or ultra-sound techniques, the radiation load on the population and on the neuroradiologists will decrease. Furthermore, using less contrast, such as gadolinium, has been shown to result in fewer deposits in the brains of patients, as well as less pollution at the ocean level, thus contributing to general well-being. Additionally, the implementation and use of artificial intelligence at many levels of the diagnostic and treatment chain will be beneficial to patients and physicians. In this paper, we discuss the place and potential not just of the techniques but of neuroradiology and the neuroradiologist as promoters of brain health and thus global health. Full article

Introduction: Prostate biopsy results form the mainstay of patient care. However, there is often significant discordance between the biopsied histology and the ‘true’ histology shown on a radical prostatectomy (RP). Discordance in pathology can lead to the mismanagement of patients, potentially missing clinically significant cancer and delaying treatment. There have been many advancements to improve the concordance of pathology and more accurately counsel patients; most notably, the induction of pre-biopsy mpMRIs has become a gold standard to aid in triaging and identifying clinically significant cancers, and also to facilitate ‘targeted’ biopsies. Although there have been multiple reviews on MRI-targeted biopsies, upgrading remains an ongoing phenomenon. Aim: To assess the rates of prostate cancer upgrading and the clinical implication of upgrading on NCCN stratification. Methods: We conducted a retrospective audit of 2994 men with non-metastatic prostate cancer diagnosed between 2010 and 2019 who progressed to a radical prostatectomy within 1 year of diagnosis without alternative cancer treatment from the multi-institutional South Australia Prostate Cancer Clinical Outcomes Collaborative registry. The study compared the histological grading between the biopsies and radical prostatectomies of men with prostate cancer and the varying rates of upgrading and downgrading for patients with and without a pre-biopsy MRI. Data were also obtain on suspected confounding variables; age, PSA, time to RP, T-stage at diagnosis and RP, number of cores, number of positive cores, prostate size, tumour volume and procedure type. The results were assessed through cross tabulation and uni- and multi-variate logistic regression while adjusting for confounders. Results: Upgrading occurred in (926) 30.9% of patients and downgrading in (458) 15.3% of patients. In total, 71% (410/579) of grade group 1 and 24.9% (289/1159) of grade group 2 were upgraded following a radical prostatectomy. By contrast, 33.4% (373/1118) of patients without prebiopsy MRI were upgraded at RP compared to 29.5% (553/1876) of the patients who received a pre-biopsy MRI. When analysed on a uni-variate level, the inclusion of a pre-biopsy MRI demonstrated a statically significant decrease in upgrading of the patient’s pathology and NCCN risk stratification (p = 0.026, OR 0.83, CI 0.71–0.98) (p = 0.049, OR 0.82, CI 0.64–1.01). However, when adjusted for confounders, the use of an MRI did not maintain a statistically significance. Conclusions: When considering the multiple variables associated with tumour upgrading, a pre-biopsy MRI did not show a statistically significant impact. However, upgrading of Gleason Grade Group following a prostatectomy is an ongoing phenomenon which can carry significant treatment implications and should remain a consideration with patients and clinicians when making decisions around treatment pathways. More research is still required to understand and improve biopsy grading to prevent further upgrading from affecting treatment choices. Full article