Search Results (247)

Background/Objectives: Cerebral blood flow (CBF) and cerebral blood volume (CBV) are critical perfusion metrics in diagnosing ischemic stroke. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables the evaluation of these cerebral perfusion metrics; however, accurately assessing them remains challenging. This study aimed to: (1) assess CBF asymmetry by quantifying and comparing it between contralateral hemispheres (right vs. left) within the MCA, ACA, and PCA territories using paired t-tests, and describe pattern of CBV; (2) evaluate overall inter-territorial regional variations in CBF across the different cerebral arterial territories (MCA, ACA, PCA), irrespective of the hemisphere, using ANOVA; (3) determine the correlation between CBF and CBV using both Pearson’s and Spearman’s correlation analyses; and (4) assess the influence of age and gender on CBF using multiple regression analysis. Methods: A cross-sectional study of 55 ischemic stroke patients was conducted. DCE-MRI was used to measure CBF and CBV. Paired t-tests compared contralateral hemispheric CBF in MCA, PCA, and ACA, one-way ANOVA assessed overall inter-territorial CBF variations, correlation analyses (Pearson/Spearman) evaluated the CBF-CBV relationship, and linear regression modeled demographic effects. Results: Significant contralateral asymmetries in CBF were observed for each cerebral pair of cerebral arteries using a paired t-test, with descriptive asymmetries noted in CBV. Separately, ANOVA revealed significant overall variability in CBF between the different cerebral arteries, irrespective of hemisphere. A strong positive correlation was found between CBF and CBV (Pearson r = 0.976; Spearman r = 0.928), with multiple regression analysis identifying age and gender as significant predictors of CBF. Conclusions: This study highlights hemispheric asymmetry and inter-territorial variation, the impact of age, and gender on CBF. DCE-MRI provides perfusion metrics that can guide individualized stroke treatment, offering valuable insights for therapeutic planning, particularly in resource-limited settings. Full article

Objective: Axillary lymph node (ALN) status in breast cancer is pivotal for guiding treatment and determining prognosis. The study aimed to explore the feasibility and efficacy of a radiomics model using voxel-wise dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-intensity-curve (TIC) profile maps to predict ALN metastasis in breast cancer. Methods: A total of 615 breast cancer patients who underwent preoperative DCE-MRI from October 2018 to February 2024 were retrospectively enrolled and randomly allocated into training (n = 430) and testing (n = 185) sets (7:3 ratio). Based on wash-in rate, wash-out enhancement, and wash-out stability, each voxel within manually segmented 3D lesions that were categorized into 1 of 19 TIC subtypes from the DCE-MRI images. Three feature sets were derived: composition ratio (type-19), radiomics features of TIC subtypes (type-19-radiomics), and radiomics features of third-phase DCE-MRI (phase-3-radiomics). Student’s t-test and the least absolute shrinkage and selection operator (LASSO) was used to select features. Four models (type-19, type-19-radiomics, type-19-combined, and phase-3-radiomics) were constructed by a support vector machine (SVM) to predict ALN status. Model performance was assessed using sensitivity, specificity, accuracy, F1 score, and area under the curve (AUC). Results: The type-19-combined model significantly outperformed the phase-3-radiomics model (AUC = 0.779 vs. 0.698, p < 0.001; 0.674 vs. 0.559) and the type-19 model (AUC = 0.779 vs. 0.541, p < 0.001; 0.674 vs. 0.435, p < 0.001) in cross-validation and independent testing sets. The type-19-radiomics showed significantly better performance than the phase-3-radiomics model (AUC = 0.764 vs. 0.698, p = 0.002; 0.657 vs. 0.559, p = 0.037) and type-19 model (AUC = 0. 764 vs. 0.541, p < 0.001; 0.657 vs. 0.435, p < 0.001) in cross-validation and independent testing sets. Among four models, the type-19-combined model achieved the highest AUC (0.779, 0.674) in cross-validation and testing sets. Conclusions: Radiomics analysis of voxel-wise DCE-MRI TIC profile maps, simultaneously quantifying temporal and spatial hemodynamic heterogeneity, provides an effective, noninvasive method for predicting ALN metastasis in breast cancer. Full article

Background/Objectives: We provide a framework for determining how far into the future the spatiotemporal dynamics of tumor growth can be accurately predicted using routinely available magnetic resonance imaging (MRI) data. Our analysis is applied to a coupled set of reaction-diffusion equations describing the spatiotemporal development of tumor cellularity and vascularity, initialized and constrained with diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data, respectively. Methods: Motivated by experimentally acquired murine glioma data, the rat brain serves as the computational domain within which we seed an in silico tumor. We generate a set of 13 virtual tumors defined by different combinations of model parameters. The first parameter combination was selected as it generated a tumor with a necrotic core during our simulated ten-day experiment. We then tested 12 additional parameter combinations to study a range of high and low tumor cell proliferation and diffusion values. Each tumor is grown for ten days via our model system to establish “ground truth” spatiotemporal tumor dynamics with an infinite signal-to-noise ratio (SNR). We then systematically reduce the quality of the imaging data by decreasing the SNR, downsampling the spatial resolution (SR), and decreasing the sampling frequency, our proxy for reduced temporal resolution (TR). With each decrement in image quality, we assess the accuracy of the calibration and subsequent prediction by comparing it to the corresponding ground truth data using the concordance correlation coefficient (CCC) for both tumor and vasculature volume fractions, as well as the Dice similarity coefficient for tumor volume fraction. Results: All tumor CCC and Dice scores for each of the 13 virtual tumors are >0.9 regardless of the SNR/SR/TR combination. Vasculature CCC scores with any SR/TR combination are >0.9 provided the SNR ≥ 80 for all virtual tumors; for the special case of high-proliferating tumors (i.e., proliferation > 0.0263 day⁻¹), any SR/TR combination yields CCC and Dice scores > 0.9 provided the SNR ≥ 40. Conclusions: Our systematic evaluation demonstrates that reaction-diffusion models can maintain acceptable longitudinal prediction accuracy—especially for tumor predictions—despite limitations in the quality and quantity of experimental data. Full article

Background/Objectives: Cerebrospinal fluid erythrocyte load (CTRED) reflects occult red-blood-cell ingress into brain/CSF and consequent heme–iron exposure, a toxic pathway relevant to Alzheimer’s disease (AD). We aimed to develop explainable machine learning (ML) models that classify high vs. low CTRED from routine, largely non-invasive inputs, and to position a blood-first workflow leveraging contemporary plasma amyloid–tau biomarkers. Methods: Twenty-six ADNI participants were analyzed. Inputs were age, sex, mean arterial pressure (MAPres), amyloid (Aβ42), total tau, phosphorylated tau, and hippocampal atrophy rate (APC) derived from longitudinal MRI. APC was computed from normalized hippocampal volumes. CTRED was binarized at the median (0 vs. >0). Data were split into train (n = 20) and held-out test (n = 6). Five classifiers (linear SVM, ridge, logistic regression, random forests, and MLP) were trained in leakage-safe pipelines with stratified five-fold cross-validation. To provide a comprehensive assessment, we presented the contribution AUC, thresholded performance metrics, summarized model performance, and the permutation feature importance (PFI). Results: On the test set, SVM, ridge, logistic regression, and random forests achieved AUC = 1.00, while the MLP achieved AUC = 0.833. Across models, PFI consistently prioritized p-tau/tau, Aβ42, and MAPres; age, sex, and APC contributed secondarily. The attribution profile aligns with mechanisms linking BBB dysfunction and amyloid-related microvascular fragility with tissue vulnerability to heme–iron. Conclusions: In this proof-of-concept study, explainable ML predicted CTRED from routine variables with biologically coherent drivers. Although ADNI measurements were CSF-based and the sample was small, the framework is non-invasive by adding plasma p-tau217/Aβ1–42 for amyloid, tau inputs, and integrating demographics, hemodynamic context, and MRI. External, plasma-based validation in larger cohorts is warranted, alongside extension to MCI and multimodal correlation (QSM, DCE-MRI) to establish clinically actionable CTRED thresholds. Full article

Quantitative magnetic resonance imaging (qMRI) denotes MRI methods that estimate physical tissue parameters in units, rather than relative signal. Typical readouts include T1/T2 relaxation (ms; or R1/R2 in s⁻¹), proton density (%), diffusion metrics (e.g., ADC in mm²/s, FA), magnetic susceptibility (χ, ppm), perfusion (e.g., CBF in mL/100 g/min; rCBV; K^trans), and regional brain volumes (cm³; cortical thickness). This review synthesizes brain qMRI across T1/T2 relaxometry, myelin/MT (MWF, MTR/MTsat/qMT), diffusion (DWI/DTI/DKI/IVIM), susceptibility imaging (SWI/QSM), perfusion (DSC/DCE/ASL), and volumetry using a unified framework: physics and signal model, acquisition and key parameters, outputs and units, validation/repeatability, clinical applications, limitations, and future directions. Our scope is the adult brain in neurodegenerative, neuro-inflammatory, neuro-oncologic, and cerebrovascular disease. Representative utilities include tracking demyelination and repair (T1, MWF/MTsat), grading and therapy monitoring in gliomas (rCBV, K^trans), penumbra and tissue-at-risk assessment (DWI/DKI/ASL), iron-related pathology (QSM), and early dementia diagnosis with normative volumetry. Persistent barriers to routine adoption are protocol standardization, vendor-neutral post-processing/QA, phantom-based and multicenter repeatability, and clinically validated cut-offs. We highlight consensus efforts and AI-assisted pipelines, and outline opportunities for multiparametric integration of complementary qMRI biomarkers. As methodological convergence and clinical validation mature, qMRI is poised to complement conventional MRI as a cornerstone of precision neuroimaging. Full article

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a functional imaging modality that can quantify tissue permeability and blood flow. Due to vasculature changes resulting from radiation therapy (RT), DCE-MRI quantitative parameters should be significantly different in regions receiving a high radiation dose compared to regions receiving a low radiation dose. This study sought to determine whether a significant difference exists in post-head-and-neck-cancer (HNC)-RT DCE-MRI quantitative parameters (K^trans and v_e) between regions of the mandible receiving a high radiation dose and regions of the mandible receiving a low radiation dose. Methods: DCE-MRI was acquired from HNC subjects post-RT. The DCE-MRI quantitative parameters K^trans and v_e were obtained through Tofts model fitting. Four mandible sections (left ramus, left body, right ramus, and right body) were delineated on subject mandible contours. Two Friedman tests comparing the mean K^trans and v_e in low-dose (≤60 Gy) areas of the four mandible regions were computed. If the Friedman test determined that a significant difference for a parameter between mandible regions exists, post hoc Wilcoxon signed-rank tests were completed comparing the four mandible regions. If the Friedman test determined that there was no significant difference between mandible regions, a Wilcoxon signed-rank test was used to determine whether a significant difference exists in the parameter between high-dose (>60 Gy) and low-dose (≤60 Gy) mandible regions. Results: 48 HNC subjects were included in the analysis. The Friedman tests showed no significant difference in v_e means between mandible regions (${\chi }_{\left(3\right)}^2$ = 1.63, p = 0.44) and a significant difference in K^trans means between mandible regions (${\chi }_{\left(3\right)}^2$ = 10.29, p = 0.005). Post hoc testing between K^trans mandible regions found that the left body and right body differed significantly from the left ramus and right ramus. The Wilcoxon signed-rank test comparing the mean v_e between high- and low-dose mandible regions found a significant difference (W = 214, p = 0.00013). Conclusions: no inherent difference in the DCE-MRI quantitative parameter v_e was observed within subject mandibles, but a significant difference was observed between v_e means in high- and low-radiation-dose mandible regions. These results provide evidence of the utility of DCE-MRI to monitor mandible vasculature changes resulting from head and neck cancer radiation therapy. Monitoring post-HNC-RT mandible vasculature changes is important to initiate earlier toxicity management and ultimately improve HNC survivors’ quality of life. Full article

Breast cancer is one of the major health threats to women worldwide; thus, a need has arisen to reduce the number of instances and deaths through new methods of diagnostic monitoring and treatment. The present review is the synthesis of the recent clinical studies and technological advances in the application of magnetic resonance imaging (MRI) to monitor the pharmacological treatment of breast cancer. The specific focus is on high-risk groups (carriers of BRCA mutations and recipients of neoadjuvant chemotherapy) and the use of novel MRI methods (dynamic contrast-enhanced (DCE) MRI, diffusion-weighted imaging (DWI), and radiomics tools). All the reviewed studies show that MRI is more sensitive (up to 95%) and specific than conventional imaging in detecting malignancy particularly in dense breast tissue. Moreover, MRI can be used to assess the response and residual disease in a tumor early and accurately for personalized treatment, de-escalate unneeded interventions, and maximize positive outcomes. AI-based radiomics combined with deep-learning models also expand the ability to predict the therapeutic response and molecular subtypes, and can mitigate the risk of overfitting models when using complex methods of modeling. Other developments are hybrid PET/MRI, image guidance during surgery, margin assessment intraoperatively, three-dimensional surgical templates, and the utilization of MRI in surgery planning and reducing reoperation. Although economic factors will always play a role, the diagnostic and prognostic accuracy and capability to aid in targeted treatment makes MRI a key tool for modern breast cancer. The growing complement of MRI and novel curative approaches indicate that breast cancer patients may experience better survival and recuperation, fewer recurrences, and a better quality of life. Full article

Introduction and Objectives: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been used as a gold standard in diagnosing and following pituitary microadenomas. However, the use of gadolinium-based contrast agents (GBCAs) involves a potential risk of long-term retention in tissues and adverse reactions. This study aimed to evaluate the sensitivity of non-contrast MRI (T1W and T2W sequences) in follow-up imaging of pituitary microadenomas, attempting a comparison with DCE-MRI, assessing tumor stability over time. Materials and methods: We retrospectively reviewed 300 pituitary MRI scans between 2020 and 2024. Included were patients with confirmed microadenomas (≤10 mm). Non-contrast (T1W/T2W) and DCE-MRI sequences were analyzed by an experienced radiologist blinded to any clinical information. Detection rates and changes in tumor size were evaluated. Results: Detection rates for 79 microadenomas were 55.7% for T1W, 70.9% for T2W, and 88.6% for DCE-MRI. There was no significant tumor growth during the follow-up (mean size 4.80 ± 2.3 mm vs. 4.81 ± 2.4 mm, p > 0.5). Conclusions: While still more sensitive for the primary diagnosis, the non-contrast MRI was able to visualize the majority of detected microadenomas, and significant growth was ruled out, thus supporting the case to omit gadolinium from follow-up imaging in stable cases. This may translate to lower costs and decreased patient risk from contrast-related hazards. Full article

Objective: The aim of this study was to evaluate the accuracy of machine and deep learning approaches on radiomics features obtained by Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and contrast enhanced mammography (CEM) in the characterization of breast cancer and in the prediction of the tumor molecular profile. Methods: A total of 153 patients with malignant and benign lesions were analyzed and underwent MRI examinations. Considering the histological findings as the ground truth, three different types of findings were used in the analysis: (1) benign versus malignant lesions; (2) G1 + G2 vs. G3 classification; (3) the presence of human epidermal growth factor receptor 2 (HER2+ vs. HER2−). Radiomic features (n = 851) were extracted from manually segmented regions of interest using the PyRadiomics platform, following IBSI-compliant protocols. Highly correlated features were excluded, and the remaining features were standardized using z-score normalization. A feature selection process based on Elastic Net regularization (α = 0.5) was used to reduce dimensionality. Synthetic balancing of the training data was applied using the ROSE method to address class imbalance. Model performance was evaluated using repeated 10-fold cross-validation and AUC-based metrics. Results: Among the 153 patients enrolled in the studies, 113 were malignant lesions. Among the 113 malignant lesions, 32 had high grading (G3) and 66 had the HER2+ receptor. Radiomic features derived from both CEM and DCE-MRI showed strong discriminative performance for malignancy detection, with several features achieving AUCs above 0.80. Gradient Boosting Machine (GBM) achieved the highest accuracy (0.911) and AUC (0.907) in differentiating benign from malignant lesions. For tumor grading, the neural network model attained the best accuracy (0.848), while LASSO yielded the highest sensitivity (0.667) for detecting high-grade tumors. In predicting HER2+ status, the neural network also performed best (AUC = 0.669), with a sensitivity of 0.842. Conclusions: Radiomics-based machine learning models applied to multiparametric CEM and DCE-MRI images offer promising, non-invasive tools for breast cancer characterization. The models effectively distinguished benign from malignant lesions and showed potential in predicting histological grade and HER2 status. These results demonstrate that radiomic features extracted from CEM and DCE-MRI, when analyzed through machine and deep learning models, can support accurate breast cancer characterization. Such models may assist clinicians in early diagnosis, histological grading, and biomarker assessment, potentially enhancing personalized treatment planning and non-invasive decision-making in routine practice. Full article

The evolving paradigm of precision medicine is redefining the landscape of orthobiologic therapies by moving beyond traditional diagnosis-driven approaches toward biologically tailored interventions. This review synthesizes current evidence supporting precision orthobiologics, emphasizing the significance of individualized treatment strategies in musculoskeletal regenerative medicine. This narrative review synthesized literature from PubMed, Embase, and Web of Science databases (January 2015–December 2024) using search terms, including ‘precision medicine,’ ‘orthobiologics,’ ‘regenerative medicine,’ ‘biomarkers,’ and ‘artificial intelligence’. Biological heterogeneity among patients with ostensibly similar clinical diagnoses—reflected in diverse inflammatory states, genetic backgrounds, and tissue degeneration patterns—necessitates patient stratification informed by molecular, genetic, and multi-omics biomarkers. These biomarkers not only enhance diagnostic accuracy but also improve prognostication and monitoring of therapeutic responses. Advanced imaging modalities such as T2 mapping, DTI, DCE-MRI, and molecular PET offer non-invasive quantification of tissue health and regenerative dynamics, further refining patient selection and treatment evaluation. Simultaneously, bioengineered delivery systems, including hydrogels, nanoparticles, and scaffolds, enable precise and sustained release of orthobiologic agents, optimizing therapeutic efficacy. Artificial intelligence and machine learning approaches are increasingly employed to integrate high-dimensional clinical, imaging, and omics datasets, facilitating predictive modeling and personalized treatment planning. Despite these advances, significant challenges persist—ranging from assay variability and lack of standardization to regulatory and economic barriers. Future progress requires large-scale multicenter validation studies, harmonization of protocols, and cross-disciplinary collaboration. By addressing these limitations, precision orthobiologics has the potential to deliver safer, more effective, and individualized care. This shift from generalized to patient-specific interventions holds promise for improving outcomes in degenerative and traumatic musculoskeletal disorders through a truly integrative, data-informed therapeutic framework. Full article

Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is emerging as a valuable tool for assessing tumor and parenchymal perfusion in the liver, playing a developing role in locoregional therapies (LRTs) for hepatocellular carcinoma (HCC). This review explores the conceptual underpinnings and early investigational stages of DCE-MRI for LRTs, including thermal ablation, transarterial chemoembolization (TACE), and transarterial radioembolization (TARE). Preclinical and early-phase studies suggest that DCE-MRI may offer valuable insights into HCC tumor microvasculature, treatment response, and therapy planning. In thermal ablation therapies, DCE-MRI provides a quantitative measurement of tumor microvasculature and perfusion, which can guide more effective energy delivery and estimation of ablation margins. For TACE, DCE-MRI parameters are proving their potential to describe treatment efficacy and predict recurrence, especially when combined with adjuvant therapies. In ⁹⁰Y TARE, DCE-MRI shows promise for refining dosimetry planning by mapping tumor blood flow to improve microsphere distribution. However, despite these promising applications, there remains a profound gap between early investigational studies and clinical translation. Current quantitative DCE-MRI research is largely confined to phantom models and initial feasibility assessments, with robust retrospective data notably lacking and prospective clinical trials yet to be initiated. With continued development, DCE-MRI has the potential to personalize LRT treatment approaches and serve as an important tool to enhance patient outcomes for HCC. Full article

MRI is a non-invasive imaging technique employed today in modern diagnostic medicine due to the fact it is capable of generating tissue architecture and function information with high image resolution without the use of ionizing radiation, unlike x-ray or CT scans. The advantages of MRI discussed in this review include better soft tissue contrast, the opportunity to perform imaging in different planes, and the ability to detect small changes in tissues, which helps to use MRI in many specialties, including cancer diagnosis and staging, as well as neurological and cardiovascular diseases. More particularly, this review aims to assess the contribution of MRI to the detection of liver cancer, especially HCC and ICC—the most frequent and aggressive types of pathology. Because of its high-resolution, MRI provides clear visualization of the small hepatic lesion and vascular mapping, which is crucial for early diagnosis and staging. It also reveals higher sensitivity and specificity than ultrasound and CT in identifying liver cancer dimensions and relations with system vasculature and a safer technique for patients who need many follow-up images. This is in addition to newer techniques that have been developed from MRI, which include the DWI, DCE-MRI, and MRE, all of which yield functional information concerning the perfusion of the tumor and the stiffness of the tissue, respectively, thus improving the diagnosis. Moreover, the application of artificial intelligence to MRI is improving lesion identification and cancer assessment, as well as patient outcome prediction, while relieving the burden of radiologists. Suggested improvements for future work include the combination of MRI with other diagnostic approaches, including circulating cell analysis and molecular imaging in managing liver cancer. Still, there is a limitation in MRI’s access globally, because scanners are expensive and unavailable in some parts of the world. Technological improvements and greater availability will extend MRI more as a valuable modality in the treatment of liver malignancies, more so for diagnosis and staging. Full article

Background/Objectives: To develop a DL-based model predicting recurrence risk in HER2-low breast cancer patients and to compare performance of the MRI-alone, clinicopathologic-alone, and combined models. Methods: We analyzed 453 patients with HER2-low breast cancer who underwent surgery and preoperative breast MRI between May 2018 and April 2022. Patients were randomly assigned to either a training cohort (n = 331) or a test cohort (n = 122). Imaging features were extracted from DCE-MRI and ADC maps, with regions of interest manually annotated by radiologists. Clinicopathological features included tumor size, nodal status, histological grade, and hormone receptor status. Three DL prediction models were developed: a CNN-based MRI-alone model, a clinicopathologic-alone model based on a multi-layer perceptron (MLP) and a combined model integrating CNN-extracted MRI features with clinicopathological data via MLP. Model performance was evaluated using AUC, sensitivity, specificity, and F1-score. Results: The MRI-alone model achieved an AUC of 0.69 (95% CI, 0.68–0.69), with a sensitivity of 37.6% (95% CI, 35.7–39.4), specificity of 87.5% (95% CI, 86.9–88.2), and F1-score of 0.34 (95% CI, 0.33–0.35). The clinicopathologic-alone model yielded the highest AUC of 0.92 (95% CI, 0.92–0.92) and sensitivity of 93.6% (95% CI, 93.4–93.8), but showed the lowest specificity (72.3%, 95% CI, 71.8–72.8) and F1-score of 0.50 (95% CI, 0.49–0.50). The combined model demonstrated the most balanced performance, achieving an AUC of 0.90 (95% CI, 0.89–0.91), sensitivity of 80.0% (95% CI, 78.7–81.3), specificity of 83.2% (95% CI: 82.7–83.6), and the highest F1-score of 0.55 (95% CI, 0.54–0.57). Conclusions: The DL-based model combining MRI and clinicopathological features showed superior performance in predicting recurrence in HER2-low breast cancer. This multimodal approach offers a framework for individualized risk assessment and may aid in refining follow-up strategies. Full article

Although intracranial hypertension (ICH) has traditionally been framed as simply a numerical escalation of intracranial pressure (ICP) and usually dealt with in its clinical form and not in terms of its complex underlying pathophysiology, an emerging body of evidence indicates that ICH is not simply an elevated ICP process but a complex process of molecular dysregulation, glymphatic dysfunction, and neurovascular insufficiency. Our aim in this paper is to provide a complete synthesis of all the new thinking that is occurring in this space, primarily on the intersection of glymphatic dysfunction and cerebral vein physiology. The aspiration is to review how glymphatic dysfunction, largely secondary to aquaporin-4 (AQP4) dysfunction, can lead to delayed cerebrospinal fluid (CSF) clearance and thus the accumulation of extravascular fluid resulting in elevated ICP. A range of other factors such as oxidative stress, endothelin-1, and neuroinflammation seem to significantly impair cerebral autoregulation, making ICH challenging to manage. Combining recent studies, we intend to provide a revised conceptualization of ICH that recognizes the nuance and complexity of ICH that is understated by previous models. We wish to also address novel diagnostics aimed at better capturing the dynamic nature of ICH. Recent advances in non-invasive imaging (i.e., 4D flow MRI and dynamic contrast-enhanced MRI; DCE-MRI) allow for better visualization of dynamic changes to the glymphatic and cerebral blood flow (CBF) system. Finally, wearable ICP monitors and AI-assisted diagnostics will create opportunities for these continuous and real-time assessments, especially in limited resource settings. Our goal is to provide examples of opportunities that exist that might augment early recognition and improve personalized care while ensuring we realize practical challenges and limitations. We also consider what may be therapeutically possible now and in the future. Therapeutic opportunities discussed include CRISPR-based gene editing aimed at restoring AQP4 function, nano-robotics aimed at drug targeting, and bioelectronic devices purposed for ICP modulation. Certainly, these proposals are innovative in nature but will require ethically responsible confirmation of long-term safety and availability, particularly to low- and middle-income countries (LMICs), where the burdens of secondary ICH remain preeminent. Throughout the review, we will be restrained to a balanced pursuit of innovative ideas and ethical considerations to attain global health equity. It is not our intent to provide unequivocal answers, but instead to encourage informed discussions at the intersections of research, clinical practice, and the public health field. We hope this review may stimulate further discussion about ICH and highlight research opportunities to conduct translational research in modern neuroscience with real, approachable, and patient-centered care. Full article

Accurate preoperative distinction between in situ and invasive Breast Cancer (BC) is critical for clinical decision-making and treatment planning. Radiomics and Machine Learning (ML) have shown promise in enhancing diagnostic performance from breast MRI, yet their application to this specific task remains underexplored. The aim of this study was to evaluate the performance of several ML classifiers, trained on radiomic features extracted from DCE–MRI and supported by basic clinical information, for the classification of in situ versus invasive BC lesions. In this study, we retrospectively analysed 71 post-contrast DCE–MRI scans (24 in situ, 47 invasive cases). Radiomic features were extracted from manually segmented tumour regions using the PyRadiomics library, and a limited set of basic clinical variables was also included. Several ML classifiers were evaluated in a Leave-One-Out Cross-Validation (LOOCV) scheme. Feature selection was performed using two different strategies: Minimum Redundancy Maximum Relevance (MRMR), mutual information. Axial 3D rotation was used for data augmentation. Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were the best-performing models, with an Area Under the Curve (AUC) ranging from 0.77 to 0.81. Notably, KNN achieved the best balance between sensitivity and specificity without the need for data augmentation. Our findings confirm that radiomic features extracted from DCE–MRI, combined with well-validated ML models, can effectively support the differentiation of in situ vs. invasive breast cancer. This approach is quite robust even in small datasets and may aid in improving preoperative planning. Further validation on larger cohorts and integration with additional imaging or clinical data are recommended. Full article