Aim/IntroductionDifferentiating malignant from inflammatory uptake on 18F-FDG PET/CT remains a major diagnostic challenge, as standardized uptake value (SUV) lacks specificity. Dynamic acquisitions with Patlak analysis can separate metabolized from unmetabolized tracer, potentially improving discrimination. We evaluated whether short-duration dynamic FDG PET/CT with Patlak parametric imaging provides complementary information beyond SUV for distinguishing malignancy from inflammation. Materials and MethodsTwenty-seven patients undergoing oncologic PET/CT (breast, lung, or gastrointestinal cancer) were included, yielding 96 lesions (69 malignant, 27 inflammatory). Short dynamic acquisitions (20 min) were motion-corrected and analysed to generate influx rate (Ki) and distribution volume (Vd) maps. Lesions were segmented on SUV images (40% SUVmax), and radiomic features were extracted from SUV, Ki, and Vd maps. Exploratory data analysis, linear modelling, and dimensionality reduction assessed separability. A Random Forest classifier was trained with crossvalidation, integrating Synthetic Minority Oversampling (SMOTE) to address class imbalance. An independent validation cohort of 15 lesions (13 inflammatory, 2 malignant) was tested. ResultsMalignant lesions showed higher SUVmean (5.8 vs. 2.8 g/ml) and Ki (1.95 vs. 0.75 ml/min/100ml), whereas inflammatory lesions demonstrated higher Vd (44.7 vs. 35.1%). No single feature provided reliable thresholds. Logistic regression achieved 89% accuracy but suffered from quasi-separation, confirming limited linear discriminability. Random Forest classification yielded robust performance (cross-validated AUC-ROC 0.876; AUC-PR 0.948). With G-mean thresholding, inflammation was detected with high recall (0.93) but recall for malignancy was lower (0.74). Feature importance highlighted SUV and Ki variance, as well as Ki/ Vd ratios, as strongest predictors. In the external validation set, accuracy reached 0.80, with inflammation reliably identified (precision 0.85, recall 0.85). ConclusionShort dynamic Patlak imaging combined with machine learning improves the characterization of malignant versus inflammatory uptake beyond SUV alone. By decomposing FDG up-take into metabolized (Ki) and unmetabolized (Vd) fractions, this approach provides physiologically meaningful separation of tracer behaviour. While sensitivity for malignancy requires further optimization, our findings establish a reproducible framework for future more extensive research on clinical interpretation of parametric imaging in oncologic PET.

Structured AbstractO_ST_ABSObjectiveC_ST_ABSAccurate cerebrospinal fluid (CSF) and brain volume estimation are important components for evaluating hydrocephalus treatments, including shunts and endoscopic third ventriculostomy (ETV) procedures. While MRI-based segmentation typically provides precise measurements, metallic artifacts from implanted shunts in hydrocephalus patients can impede accurate volume determination. This study introduces a method for assessing brain growth in hydrocephalus patients using artifact-affected MRI scans and presents an efficient, automated artificial intelligence (AI)-based pipeline for hemi-brain segmentation and subsequent volume assessment. MethodsThe study consists of 75 patients participating in the Endoscopic versus Shunt Treatment of Hydrocephalus in Infants (ESTHI) trial. Pre- and post-operative T2 MRI scans were collected. Hemi-brain growth curves for the artifact-free hemisphere are proposed to assess postoperative brain growth from MRI with metallic shunt artifacts. An AI-based hemi-brain volume estimation pipeline was developed, consisting of a brain/CSF segmentation model and a hemi-brain mask generator. Segmentation labels, including left/right hemi-brain masks and brain/CSF segmentation maps were created. The AI pipeline was trained and validated using a manually segmented data subset. The volumes of left and right brain hemispheres after surgeries were calculated and analyzed. ResultsPostoperative hemisphere volume ratios approach the normal ratio and remained constant over time, confirming the feasibility for use of hemi-brain measurements as proxies for whole-brain volume assessment in the presence of metallic artifacts. Additionally, the AI-based pipeline demonstrated high accuracy in generating hemi-brain masks and segmenting brain/CSF, effectively automating the process of hemi-brain volume estimation. ConclusionsThe hemi-brain volume estimation of the unaffected hemisphere offers a feasible method for assessing brain growth over time. This process can be automated using a highly accurate AI pipeline, providing a valuable tool for monitoring brain growth in pediatric hydrocephalus patients with shunts.

1The Brain Imaging and Neurophysiology Database (BIND) represents one of the largest multi-institutional, multimodal, clinical neuroimaging repositories, comprising 1.8 million brain scans from 38,945 patients, linked to neurophysiological recordings. This comprehensive dataset addresses critical limitations in neuroimaging research by providing unprecedented scale and diversity across pathologies and health. BIND integrates de-identified data from Massachusetts General Hospital, Brigham and Womens Hospital, and Stanford University, including 1,723,699 MRI scans (1.5 Tesla, 3 Tesla, and 7 Tesla), 54,137 CT scans, 5,093 PET scans, and 526 SPECT scans, converted to standardized NIfTI format following BIDS organization. The database spans the full age spectrum (newborn to 106 years) and encompasses diverse neurological conditions alongside healthy patients. We deployed Bio-Medical Large Language Models to extract structured clinical metadata from 84,960 brain-related reports, categorizing findings into standardized pathology classifications. All imaging data are linked to previously published EEG and polysomnography recordings from the Harvard Electroencephalography Database, enabling unprecedented multimodal analyses. BIND is freely accessible for academic research through the Brain Data Science Platform (https://bdsp.io/). This resource facilitates large-scale neuroimaging studies, machine learning applications, and multimodal brain research to accelerate discoveries in clinical neuroscience.

Accurate detection of breast lesion type is crucial for optimizing treatment; however, due to the limited precision of current diagnostic methods, biopsies are often required. To address this limitation, we proposed radio frequency time series dynamic processing (RFTSDP) in 2020, which analyzes the dynamic response of tissue and the impact of scatterer displacement on RF echoes during controlled stimulations to enhance diagnostic information. We developed a vibration-generating device and collected ultrafast ultrasound data from 11 ex vivo breast tissue samples under different stimulations. Deep learning (DL) was used for automated feature extraction and lesion classification into 2, 3, and 5 categories. The performance of the convolutional neural network (CNN)-based RFTSDP method was compared with traditional machine learning techniques, which involved spectral and nonlinear feature extraction from RF time series, followed by a support vector machine (SVM). With 65 Hz vibration, the DL-based RFTSDP method achieved 99.53 ± 0.47% accuracy in classifying and grading breast lesions. CNN consistently outperformed SVM, particularly under vibratory stimulation. In 5-class classification, CNN reached 98.01% versus 95.64% for SVM, with the difference being statistically significant (P < .05). Furthermore, the CNN-based RFTSDP method showed a 28.67% improvement in classification accuracy compared to the non-stimulation condition and the analysis of focused raw data. We developed a DL-based CAD system capable of classifying and grading breast lesions. This study demonstrates that the proposed system not only enhances classification but also ensures increased stability and robustness compared to traditional methods.

Artificial intelligence (AI) is having an increasing impact on healthcare. In ultrasound-guided regional anesthesia (UGRA), commercially available devices exist that augment traditional grayscale ultrasound imaging by highlighting key sono-anatomical structures in real-time. We review the latest evidence supporting this emerging technology and consider the opportunities and challenges to its widespread deployment. The existing literature is limited and heterogenous, which impedes full appraisal of systems, comparison between devices, and informed adoption. AI-based devices promise to improve clinical practice and training in UGRA, though their impact on patient outcomes and provision of UGRA techniques is unclear at this early stage. Calls for standardization across both UGRA and AI are increasing, with greater clinical leadership required. Emerging AI applications in UGRA warrant further study due to an opaque and fragmented evidence base. Robust and consistent evaluation and reporting of algorithm performance, in a representative clinical context, will expedite discovery and appropriate deployment of AI in UGRA. A clinician-focused approach to the development, evaluation, and implementation of this exciting branch of AI has huge potential to advance the human art of regional anesthesia.

Predictive tools for stratifying neonatal hydrocephalus into low- and high-risk groups for cerebrospinal fluid (CSF) diversion are currently lacking. We developed and validated an artificial intelligence (AI) model that integrates multimodal imaging and clinical data to predict CSF diversion needs. The development cohort included 116 neonates with suspicion of raised intracranial pressure (ICP) from a Chinese tertiary referral hospital (80 with intracranial pressure > 80 mm H₂O, 36 with intracranial pressure ≤ 80 mm H₂O). The external validation cohort consisted of 21 neonates with hydrocephalus from an American medical center, categorized by etiology: prenatal myelomeningocele (MMC) closure (n = 5), postnatal MMC closure (n = 6), and post-hemorrhagic hydrocephalus (PHH) (n = 10). Inclusion criteria required available MRI and complete clinical follow-up to confirm CSF diversion outcomes. The primary outcome was the need for CSF diversion. Model performance was assessed using under the receiver operating characteristics curve (AUC), sensitivity, and specificity. The hybrid AI model achieved an AUC of 0.824 in the development cohort in predicting raised ICP, outperforming both the clinical-only model (AUC 0.528, p < 0.001) and the image-only model (AUC 0.685, p = 0.007). In the external validation cohort, the fused MRI-based model achieved an AUC of 0.808. The model correctly predicted CSF diversion in 4/5 prenatal MMC, 4/6 postnatal MMC, and 9/10 PHH cases. The AI model demonstrated robust performance in predicting the need for CSF diversion, particularly in PHH cases, and has the potential to assist decision-making, especially in settings with limited pediatric neurosurgical expertise. Future work should focus on further refining model performance for complex etiologies such as MMC-associated hydrocephalus.

This review highlights recent advancements and evidence-based approaches in the critical care management of aneurysmal subarachnoid hemorrhage (aSAH), focusing on developments from the past 18 months. It addresses key challenges [rebleeding prevention, delayed cerebral ischemia (DCI), hydrocephalus, transfusion strategies, and temperature management], emphasizing multidisciplinary care and personalized treatment. Recent studies underscore the importance of systolic blood pressure control (<160 mmHg) to reduce rebleeding risk before aneurysm securing. Novel prognostic tools, including the modified 5-item frailty index and quantitative imaging software, show promise in improving outcome prediction. Prophylactic lumbar drainage may reduce DCI and improve neurological outcomes, while milrinone and computed tomography perfusion-guided therapies are being explored for vasospasm management. Transfusion strategies suggest a hemoglobin threshold of 9 g/dl may optimize outcomes. Temperature management remains contentious, but consensus recommends maintaining normothermia (36.0-37.5 °C) with continuous monitoring. Advances in aSAH care emphasize precision medicine, leveraging technology [e.g. Artificial intelligence (AI), quantitative imaging], and multidisciplinary collaboration. Key unresolved questions warrant multicenter trials to validate optimal blood pressure, transfusion, and temperature targets alongside emerging therapies for DCI.

Neurodegeneration occurs when the body's central nervous system becomes impaired as a person ages, which can happen at an accelerated pace. Neurodegeneration impairs quality of life, affecting essential functions, including memory and the ability to self-care. Genetics play an important role in neurodegeneration and longevity. Brain age gap estimation (BrainAGE) is a biomarker that quantifies the difference between a machine learning model-predicted biological age of the brain and the true chronological age for healthy subjects; however, a large portion of the variance remains unaccounted for in these models, attributed to individual differences. This study focuses on predicting the BrainAGE more accurately, aided by genetic information associated with neurodegeneration. To achieve this, a BrainAGE model was developed based on MRI measures, and then the associated genes were determined with a Genome-Wide Association Study. Subsequently, genetic information was incorporated into the models. The incorporation of genetic information yielded improvements in the model performances by 7% to 12%, showing that the incorporation of genetic information can notably reduce unexplained variance. This work helps to define new ways of determining persons susceptible to neurological aging decline and reveals genes for targeted precision medicine therapies.

Magnetic resonance imaging (MRI) offers multiple sequences that provide complementary views of brain anatomy and pathology. However, real-world datasets often exhibit variability in sequence availability due to clinical and logistical constraints. This variability complicates radiological interpretation and limits the generalizability of machine learning models that depend on a consistent multimodal input. Here, we propose an anatomy-guided, modality-agnostic framework to assess disease-related abnormalities in brain MRI, leveraging structural context to ensure robustness in diverse input configurations. Central to our approach is Region ModalMix (RMM), an augmentation strategy that integrates anatomical priors during training to improve model performance under missing or variable modality conditions. Using the BraTS 2020 dataset (n = 369), our framework outperformed state-of-the-art methods, achieving a 9.68 mm average reduction in 95th percentile Hausdorff Distance (HD95) and a 1.36 percentage point improvement in Dice Similarity Coefficient (DSC) over baselines with only one available modality. To evaluate out-of-distribution generalization, we tested RMM on the MU-Glioma-Post dataset (n = 593), which includes heterogeneous post-operative glioma cases. Despite distribution shifts, RMM maintained strong performance, reducing HD95 by 18.24 mm and improving DSC by 9.54% points in the most severe missing-modality scenario. Our framework is applicable to multimodal neuroimaging pipelines, enabling more generalizable abnormality detection under heterogeneous data availability.

Emerging evidence suggests that blood-oxygen-level-dependent signals in white matter reflect functional activity; however, it remains unclear whether white matter function is altered in rhegmatogenous retinal detachment (RRD) and how it interacts with gray matter. We conducted resting-state functional MRI analyses in patients with RRD and healthy controls to investigate regional white matter activity using amplitude of low-frequency fluctuations/fractional ALFF (ALFF/fALFF), and cross-tissue white matter-gray matter functional connectivity. Voxel-wise analyses were performed to identify aberrant white matter regions, and seed-based connectivity mapping was applied using affected white matter tracts. Support vector machine models were constructed to evaluate the diagnostic utility of these functional features. Patients with RRD exhibited significantly increased ALFF/fALFF in key projection fibers, including the bilateral anterior corona radiata (ACR) and anterior limb of the internal capsule (ALIC). Enhanced functional connectivity was observed between the left ACR and nonvisual gray matter regions such as the right middle temporal gyrus and medial orbitofrontal cortex. Among all features, the fALFF value of the left ALIC demonstrated the highest classification performance (area under the curve = 0.8974) in distinguishing RRD from healthy controls. These findings reveal aberrant spontaneous low-frequency oscillatory activity and enhanced white matter-gray matter coupling in patients with RRD, reflecting cross-tissue functional reorganization beyond the retina. Notably, the elevated fALFF signal in the left ALIC demonstrates strong potential as a neuroimaging biomarker. This study underscores the value of white matter functional metrics in characterizing central nervous system alterations in RRD and offers novel insights into its neurobiological underpinnings.