AJNR Am J Neuroradiol. 2025 Apr 7:ajnr.A8768. doi: 10.3174/ajnr.A8768. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Perfusion and perfusion-related parameter maps obtained using dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are both useful for clinical diagnosis and research. However, using both DSC and DCE MRI in the same scan session requires two doses of gadolinium contrast agent. The objective was to develop deep-learning based methods to synthesize DSC-derived parameter maps from DCE MRI data.

MATERIALS AND METHODS: Independent analysis of data collected in previous studies was performed. The database contained sixty-four participants, including patients with and without brain tumors. The reference parameter maps were measured from DSC MRI performed following DCE MRI. A conditional generative adversarial network (cGAN) was designed and trained to generate synthetic DSC-derived maps from DCE MRI data. The median parameter values and distributions between synthetic and real maps were compared using linear regression and Bland-Altman plots.

RESULTS: Using cGAN, realistic DSC parameter maps could be synthesized from DCE MRI data. For controls without brain tumors, the synthesized parameters had distributions similar to the ground truth values. For patients with brain tumors, the synthesized parameters in the tumor region correlated linearly with the ground truth values. In addition, areas not visible due to susceptibility artifacts in real DSC maps could be visualized using DCE-derived DSC maps.

CONCLUSIONS: DSC-derived parameter maps could be synthesized using DCE MRI data, including susceptibility-artifact-prone regions. This shows the potential to obtain both DSC and DCE parameter maps from DCE MRI using a single dose of contrast agent.

ABBREVIATIONS: cGAN=conditional generative adversarial network; Ktrans=volume transfer constant; rCBV=relative cerebral blood volume; rCBF=relative cerebral blood flow; Ve=extravascular extracellular volume; Vp=plasma volume.

PMID:40194853 | DOI:10.3174/ajnr.A8768

AJNR Am J Neuroradiol. 2025 Apr 7:ajnr.A8770. doi: 10.3174/ajnr.A8770. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Spinal cord injury (SCI) in the pediatric population presents a unique challenge in diagnosis and prognosis due to the complexity of performing clinical assessments on children. Accurate evaluation of structural changes in the spinal cord is essential for effective treatment planning. This study aims to evaluate structural characteristics in pediatric patients with SCI by comparing cross-sectional area (CSA), anterior-posterior (AP) width, and right-left (RL) width across all vertebral levels of the spinal cord between typically developing (TD) and participants with SCI. We employed deep learning techniques to utilize these measures for detecting SCI cases and determining their injury severity.

MATERIALS AND METHODS: Sixty-one pediatric participants (ages 6-18), including 20 with chronic SCI and 41 TD, were enrolled and scanned using a 3T MRI scanner. All SCI participants underwent the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) test to assess their neurological function and determine their American Spinal Injury Association (ASIA) Impairment Scale (AIS) category. T2-weighted MRI scans were utilized to measure CSA, AP width, and RL widths along the entire cervical and thoracic cord. These measures were automatically extracted at every vertebral level of the spinal cord using the SCT toolbox. Deep convolutional neural networks (CNNs) were utilized to classify participants into SCI or TD groups and determine their AIS classification based on structural parameters and demographic factors such as age and height.

RESULTS: Significant differences (p<0.05) were found in CSA, AP width, and RL width between SCI and TD participants, indicating notable structural alterations due to SCI. The CNN-based models demonstrated high performance, achieving 96.59% accuracy in distinguishing SCI from TD participants. Furthermore, the models determined AIS category classification with 94.92% accuracy.

CONCLUSIONS: The study demonstrates the effectiveness of integrating cross-sectional structural imaging measures with deep learning methods for classification and severity assessment of pediatric SCI. The deep learning approach outperforms traditional machine learning models in diagnostic accuracy, offering potential improvements in patient care in pediatric SCI management.

ABBREVIATIONS: SCI = Spinal Cord Injury, TD = Typically Developing, CSA = Cross-Sectional Area, AP = Anterior-Posterior, RL = Right-Left, ASIA = American Spinal Injury Association, AIS = American Spinal Injury Association, CNN = Convolutional Neural Network.

PMID:40194851 | DOI:10.3174/ajnr.A8770

J Mol Biol. 2025 Apr 5:169131. doi: 10.1016/j.jmb.2025.169131. Online ahead of print.

ABSTRACT

With the rise of small interfering RNA (siRNA) as a therapeutic tool, effective siRNA design is crucial. Current methods often emphasize sequence-related features, overlooking structural information. To address this, we introduce ENsiRNA, a multimodal approach utilizing a geometric graph neural network to predict the efficacy of both standard and modified siRNA. ENsiRNA integrates sequence features from a pretrained RNA language model, structural characteristics, and thermodynamic data or chemical modification to enhance prediction accuracy. Our results indicate that ENsiRNA outperforms existing methods, achieving over a 13% improvement in Pearson Correlation Coefficient (PCC) for standard siRNA across various tests. For modified siRNA, despite challenges associated with RNA folding methods, ENsiRNA still demonstrates competitive performance in different datasets. This novel method highlights the significance of structural information and multimodal strategies in siRNA prediction, advancing the field of therapeutic design.

PMID:40194620 | DOI:10.1016/j.jmb.2025.169131

Int J Biol Macromol. 2025 Apr 5:142871. doi: 10.1016/j.ijbiomac.2025.142871. Online ahead of print.

ABSTRACT

The accurate prediction of inhibitor-kinase binding affinity is pivotal for advancing drug development and precision medicine. In this study, we developed predictive models for human kinases, including cyclin-dependent kinases (CDKs), mitogen-activated protein kinases (MAP kinases), glycogen synthase kinases (GSKs), CDK-like kinases (CMGC kinase group) and receptor tyrosine kinases (RTKs)-key regulators of cellular signaling and disease progression. These kinases serve as primary drug targets in cancer and other critical diseases. To enhance affinity prediction precision, we introduce an innovative multimodal fusion model, KinNet. The model integrates the GraphKAN network, which effectively captures both local and global structural features of drug molecules. Furthermore, it leverages kernel functions and learnable activation functions to dynamically optimize node and edge feature representations. Additionally, the model incorporates the Conv-Enhanced Mamba module, combining Conv1D's ability to capture local features with Mamba's strength in processing long sequences, facilitating comprehensive feature extraction from protein sequences and molecular fingerprints. Experimental results confirm that the KinNet model achieves superior prediction accuracy compared to existing approaches, underscoring its potential to elucidate inhibitor-kinase binding mechanisms. This model serves as a robust computational framework to support drug discovery and the development of kinase-targeted therapies.

PMID:40194581 | DOI:10.1016/j.ijbiomac.2025.142871

Phys Med Biol. 2025 Apr 7. doi: 10.1088/1361-6560/adc9f8. Online ahead of print.

ABSTRACT

When it comes to the implementation of Deep-Learning (DL) based Breast Cancer Risk (BCR) prediction models in clinical settings, it is important to be aware that these models could be sensitive to various factors, especially those arising from the acquisition process. In this work, we investigated how sensitive the state-of-the-art BCR prediction model is to realistic image alterations that can occur as a result of different positioning during the acquisition process.&#xD;&#xD;Approach: 5076 mammograms (1269 exams, 650 participants) from the Slovenian and Belgium (University Hospital Leuven) Breast Cancer Screening Programs were collected. The Original MIRAI model was used for 1-5-year BCR estimation. First, BCR was predicted for the original mammograms, which were not changed. Then, a series of different image alteration techniques was performed, such as swapping left and right breasts, removing tissue below the inframammary fold, translations, cropping, rotations, registration and pectoral muscle removal. In addition, a subset of 81 exams, where at least one of the mammograms had to be retaken due to inadequate image quality, served as an approximation of a test-retest experiment. Bland-Altman plots were used to determine prediction bias and 95% limits of agreement (LOA). Additionally, the mean absolute difference in BCR (Mean AD) was calculated. The impact on the overall discrimination performance was evaluated with the AUC.&#xD;&#xD;Results: Swapping left and right breasts had no impact on the predicted BCR. The removal of skin tissue below the inframammary fold had minimal impact on the predicted BCR (1-5-year LOA: [-0.02, 0.01]). The model was sensitive to translation, rotation, registration, and cropping, where LOAs of up to ±0.1 were observed. Partial pectoral muscle removal did not have a major impact on predicted BCR, while complete removal of pectoral muscle introduced substantial prediction bias and LOAs (1-year LOA: [-0.07, 0.04], 5-year LOA: [-0.06, 0.03]). The approximation of a real test-retest experiment resulted in LOAs similar to those of simulated image alterations. None of the alterations impacted the overall BCR discrimination performance; the initial 1-year AUC (0.90 [0.88, 0.92]) and 5-year AUC (0.77 [0.75, 0.80]) remained unchanged.&#xD;&#xD;Significance: While tested image alterations do not impact overall BCR discrimination performance, substantial changes in predicted 1-5-year BCR can occur on an individual basis.

PMID:40194545 | DOI:10.1088/1361-6560/adc9f8

Biomed Phys Eng Express. 2025 Apr 7. doi: 10.1088/2057-1976/adc9b6. Online ahead of print.

ABSTRACT

&#xD;Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. However, conventional methods for quantifying CTP images, such as singular value decomposition (SVD), often lead to oscillations in the estimated residue function and underestimation of tissue perfusion. In addition, the use of global arterial input function (AIF) potentially leads to erroneous parameter estimates. We aim to develop a method for accurately estimating physiological parameters from CTP images.&#xD;Approach:&#xD;We introduced a Transformer-based network to learn voxel-wise temporal features of CTP images. With global AIF and concentration time curve (CTC) of brain tissue as inputs, the network estimated local AIF and flow-scaled residue function. The derived parameters, including cerebral blood flow (CBF) and bolus arrival delay (BAD), were validated on both simulated and patient data (ISLES18 dataset), and were compared with multiple SVD-based methods, including standard SVD (sSVD), block-circulant SVD (cSVD) and oscillation-index SVD (oSVD).&#xD;Main results:&#xD;On data simulating multiple scenarios, local AIF estimated by the proposed method correlated with true AIF with a coefficient of 0.97±0.04 (P<0.001), estimated CBF with a mean error of 4.95 ml/100 g/min, and estimated BAD with a mean error of 0.51 s; the latter two errors were significantly lower than those of the SVD-based methods (P<0.001). The CBF estimated by the SVD-based methods were underestimated by 10%~15%. For patient data, the CBF estimates of the proposed method were significantly higher than those of the sSVD method in both normally perfused and ischemic tissues, by 13.83 ml/100 g/min or 39.33% and 8.55 ml/100 g/min or 57.73% (P<0.001), respectively, which was in agreement with the simulation results.&#xD;Significance:&#xD;The proposed method is capable of estimating local AIF and perfusion parameters from CTP images with high accuracy, potentially improving CTP's performance and efficiency in diagnosing and treating ischemic stroke.&#xD.

PMID:40194529 | DOI:10.1088/2057-1976/adc9b6

Biomed Phys Eng Express. 2025 Apr 7. doi: 10.1088/2057-1976/adc9b7. Online ahead of print.

ABSTRACT

&#xD;This study is focused on creating an effective glaucoma detection system employing a Hybrid Centric Convolutional Neural Network (HCCNN) model. By using Particle Swarm Optimization (PSO), classification accuracy is increased and computing complexity is reduced. Modified U-Net is also used to segment the optic disc (OD) and optic cup (OC) regions of classified glaucoma images in order to determine the severity of glaucoma.&#xD;Methods:&#xD;The proposed HCCNN model can extract features from fundus images that show signs of glaucoma. To improve the model performance, hyperparameters like dropout rate, learning rate, and the number of units in dense layer are optimized using the PSO method. The PSO algorithm iteratively assesses and modifies these parameters to minimise classification error.The classified glaucoma image is subjected to channel separation to enhance the visibility of relevant features. This channel separated image is segmented using the modified U-Net to delineate the OC and OD regions.&#xD;Results:&#xD;Experimental findings indicate that the PSO-HCCNN model attains classification accuracy of 94% and 97% on DRISHTI-GS and RIM-ONE datasets. Performance criteria including accuracy, sensitivity, specificity, and AUC are employed to assess the system's efficacy, demonstrating a notable enhancement in the early detection rates of glaucoma. To evaluate the segmentation performance, parameters such as Dice coefficient, and Jaccard index are computed.&#xD;Conclusion:&#xD;The integration of PSO with the HCCNN model considerably enhances glaucoma detection from fundus images by optimising essential parameters and accurate OD and OC segmentation, resulting in a robust and precise classification model. This method has potential uses in ophthalmology and may help physicians detect glaucoma early and accurately.&#xD.

PMID:40194525 | DOI:10.1088/2057-1976/adc9b7

Genetics. 2025 Apr 7:iyaf068. doi: 10.1093/genetics/iyaf068. Online ahead of print.

ABSTRACT

We introduce a framework for using contrastive learning for dimensionality reduction on genetic datasets to create PCA-like population visualizations. Contrastive learning is a self-supervised deep learning method that uses similarities between samples to train the neural network to discriminate between samples. Many of the advances in these types of models have been made for computer vision, but some common methodology does not translate well from image to genetic data. We define a loss function that outperforms loss functions commonly used in contrastive learning, and a data augmentation scheme tailored specifically towards SNP genotype datasets. We compare the performance of our method to PCA and contemporary non-linear methods with respect to how well they preserve local and global structure, and how well they generalize to new data. Our method displays good preservation of global structure and has improved generalization properties over t-SNE, UMAP, and popvae, while preserving relative distances between individuals to a high extent. A strength of the deep learning framework is the possibility of projecting new samples and fine-tuning to new datasets using a pre-trained model without access to the original training data, and the ability to incorporate more domain-specific information in the model. We show examples of population classification on two datasets of dog and human genotypes.

PMID:40194517 | DOI:10.1093/genetics/iyaf068

J Pharm Biomed Anal. 2025 Apr 3;262:116868. doi: 10.1016/j.jpba.2025.116868. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis is a high-mortality chronic lung disease, and currently existing medications have limited therapeutic efficacy with noticeable adverse effects, urgently necessitating the exploration of more effective and safer treatment options. Our preliminary studies have demonstrated that the leech extract group with molecular weight greater than 10 kDa (>10 kDa group) exhibited superior anti-idiopathic pulmonary fibrosis efficacy. To trace the active components of > 10 kDa group, it was separated by gel electrophoresis and analyzed by Nano LC-MS/MS. To further analyze the effects of these active components on the regulation of metabolic pathways in fibrotic lung tissue, the metabolites of lung tissue were analyzed by UPLC/MS after administration of > 10 kDa group in bleomycin-induced pulmonary fibrosis (BML-induced PF) mice for 28 days at a 0.179 mg/g per day. A total of 17 proteins were identified in > 10 kDa group and 46 endogenous metabolites were identified in lung tissue, among which 18 significantly differential metabolites were screened as potential metabolomics biomarkers. Metabolic pathway analysis demonstrated that these identified differential metabolites mainly involved biosynthesis of unsaturated fatty acids, phenylalanine-tyrosine and tryptophan biosynthesis and tryptophan metabolism signaling pathway, indicating that the active components of > 10 kDa group mainly regulated the metabolic disorders of lung tissue in BLM-induced mice by up-regulating the biosynthesis of unsaturated fatty acids, down-regulating phenylalanine-tyrosine and tryptophan biosynthesis, and adjusting tryptophan metabolism signaling pathway.

PMID:40194473 | DOI:10.1016/j.jpba.2025.116868

Can J Urol. 2025 Mar 18;32(1):29-36. doi: 10.32604/cju.2025.064710.

ABSTRACT

INTRODUCTION: Non-muscle-invasive bladder cancer (NMIBC) is a common malignancy worldwide. While Bacillus Calmette-Guérin (BCG) is standard of care for treatment for most patients with high-risk NMIBC, many will either not respond to BCG initially or will eventually develop BCG-unresponsive disease. A treatment option in BCG-unresponsive disease is nadofaragene firadenovec-vncg (Adstiladrin), a nonreplicating adenoviral vector-based gene therapy approved by the US Food and Drug Administration (FDA) for the treatment of adults with high-risk BCG-unresponsive NMIBC with carcinoma in situ with or without papillary tumors.

OBJECTIVE: To review safety outcomes of participants who received the FDA-approved dose of nadofaragene firadenovec (3 × 1011 vp/mL) across phase 2 (NCT01687244) and phase 3 (NCT02773849) studies.

METHODS: Data from the phase 2 and phase 3 studies were collected and analyzed. The findings were reported using descriptive statistics to summarize the key outcomes observed across studies.

RESULTS: Common adverse events (AEs) among nadofaragene firadenovec recipients were leakage of fluid around the urinary catheter, fatigue, bladder spasm, chills, dysuria, and micturition urgency. Most study drug-related AEs were mild and localized, with no grade 4 or 5 study drug-related AEs observed in either study. Study drug-related AEs were generally transient, with most study drug-related AEs having a median duration of ≤2.0 days in the phase 3 study. Discontinuation rates due to study drug-related AEs were low, with none (0%) in the phase 2 study and three (1.9%) in the phase 3 study. No specific postmarketing surveillance was required by the FDA besides routine pharmacovigilance monitoring; no new real-world safety signals have been observed.

CONCLUSION: Nadofaragene firadenovec demonstrated a favorable and tolerable safety profile across its clinical study program, allowing for broad patient selection among those with high-risk BCG-unresponsive NMIBC.

PMID:40194933 | DOI:10.32604/cju.2025.064710

Br J Clin Pharmacol. 2025 Apr 7. doi: 10.1002/bcp.70064. Online ahead of print.

ABSTRACT

AIMS: The incidence of osteoporosis is projected to exceed 70 million people over the age of 65 years by 2030. Osteoanabolic agents, such as teriparatide and abaloparatide, are not only effective in reducing fracture incidence but also improve skeletal microstructure-an important need not met by antiresorptive agents. However, anabolic agents must be administered by daily subcutaneous injections which can be a challenge in older women. To address this need, we have developed an oral robotic pill (RP) designed to deliver biotherapeutics safely and painlessly.

METHODS: This report describes the results of a 2-part Phase 1 study conducted to evaluate the safety, tolerability and pharmacokinetics of single (Part 1) and repeat doses (Part 2) of teriparatide delivered via the RP (RT-102) in healthy and postmenopausal women.

RESULTS: Teriparatide, administered by the RP, was measurable in 26/29 and 63/69 of participants in Part 1 and Part 2, respectively. RT-102 at the 20-μg dose yielded a lower maximum observed serum concentration (98 ± 10 vs. 128 ± 20 pg mL-1), delayed time to reach maximum observed serum concentration (68 ± 15 vs. 13 ± 2 min) and higher area under the curve to infinity (342 ± 44 vs. 126 ± 29 h pg mL-1) resulting in a 3-fold higher bioavailability than subcutaneous injection. RT-102 was well tolerated with only 5 mild to moderate adverse events (AEs) related to the RP that resolved without intervention and no serious AEs. Drug-related AEs were similar in severity and frequency between RT-102 and subcutaneous teriparatide.

CONCLUSION: These data demonstrate that RT-102 can safely and reliably deliver therapeutic levels of teriparatide.

CLINICALTRIALS: GOV: NCT#05164614.

PMID:40194767 | DOI:10.1002/bcp.70064

Medicine (Baltimore). 2025 Apr 4;104(14):e42031. doi: 10.1097/MD.0000000000042031.

ABSTRACT

A laboratory-initiated preemptive and reactive cytochrome P450 and SLCO1B1 PGx testing protocol was evaluated in a private toxicology laboratory with the intent of identifying enzyme frequencies and associated adverse drug events. This study involved non-experimental observational research. During the retrospective medical chart review, patient demographics, statements of medical necessity, and PGx testing data were collected. Frequencies and percentages were calculated for the collected data, and statistical analysis was performed using Intellectus online software. A total of 192 PGx patient records from September 2019 to October 2021 were retrospectively reviewed. For patient demographics, men (n = 118; (61%)) were the majority gender identified among the patient population and Caucasians (n = 112; (58%)) followed by African Americans (n = 37; (19%)) were the most identified ancestry. The mean age of the patients was 69 (±9) years. CYP1A2 hyperinducers, followed by CYP3A5 poor metabolizers and CYP2B6 intermediate metabolizers, are the most encountered cytochrome P450 and SLCO1B1 enzymes. Regarding drug-gene interactions, 41 patients had 1 interaction, 29 had 2, and 31 had 3 or more interactions. For drug-drug interactions, 35 patients had 1 interaction, 15 had 2, and 30 had 3 or more interactions. Overall, 123 patients showed a minor or greater impact on drug-drug or drug-gene interactions. Overall, our study identified cytochrome P450 and SCLCO1B1 enzyme frequencies and patients experiencing actionable adverse drug events. By raising awareness of PGx test results through individualized clinician training, education, and interventions, these adverse events can be promptly identified and resolved.

PMID:40193664 | DOI:10.1097/MD.0000000000042031

J Clin Psychopharmacol. 2025 Apr 7. doi: 10.1097/JCP.0000000000002001. Online ahead of print.

ABSTRACT

BACKGROUND: Pharmacogenomic studies on antidepressant treatment outcomes could be conducted using previously collected data from electronic health record (EHR)-linked biobanks. However, absence of EHR based outcome measures is an unmet need in designing such studies We aimed to define EHR-derived antidepressant outcome measures and explore their utility in showing associations between treatment outcomes and Cytochrome P450 (CYP) metabolizer phenotypes in a proof-of-concept study.

METHODS: Using data from the EHR-linked cohort, Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT 10K) Study, we collected prescription data and patient health questionnaire 9 (PHQ-9) scores to compute 3 proxy measures for antidepressant response, efficacy, and acceptability: change in PHQ-9 scores, longest treatment interval with a single antidepressant, and antidepressant non-refill. Subsequently, we tested the association of both prescription-based outcomes with DNA-predicted CYP metabolizer phenotypes in European-ancestry participants.

RESULTS: We identified 3920 RIGHT 10K participants with at least 1 antidepressant prescription and European-ancestry. Participants had a mean age of 61 years and 72% were women. Implementation of the PHQ-9 outcome was not feasible because of missingness. Of both prescription-based outcomes, antidepressant non-refill reproduced several known antidepressant-CYP interactions. However, the pilot was limited by small subgroups of participants with non-normal metabolizer phenotypes.

CONCLUSIONS: Derived from structured data, antidepressant non-refill is a promising outcome measure for EHR-linked biobanks that partially reproduced antidepressant-CYP interactions. However, testing on larger datasets is necessary to understand whether it would be a useful for pharmacogenomic research.

PMID:40193626 | DOI:10.1097/JCP.0000000000002001

Neurology. 2025 Apr 8;104(7_Supplement_1):3302. doi: 10.1212/WNL.0000000000210927. Epub 2025 Apr 7.

ABSTRACT

OBJECTIVE: To use concept mapping to ascertain preferences of people with epilepsy of child-bearing potential (PWECP) ages 14-26 years and caregivers for reproductive health counseling by neurologists.

BACKGROUND: The American Academy of Neurology (AAN) recommends that neurologists counsel PWECP ages 12-44 years old annually about at least two of three topics: folic acid, interactions between antiseizure medications (ASMs) and contraceptives, and ASM effects on pregnancy and/or fetal/child development. However, guideline development did not include the perspectives of younger PWECP or caregivers.

DESIGN/METHODS: We recruited PWECP ages 14-26 years and caregivers from one institution's child neurology clinics, a research registry, and epilepsy-related listservs. Participants: 1) generated topics about epilepsy and reproductive health important for neurologist counseling of PWECP ages 14-26 years, 2) sorted topics into conceptually-related categories, and 3) rated topics' importance on 5-point Likert scales for PWECP ages 14-17 and 18-26 years.

RESULTS: Thirty-four PWECP and 20 caregivers generated 37 topics, which were sorted/rated by 35 PWECP and 23 caregivers. Consensus categories included "Contraception," "Hormonal changes" (including hormonal influences on seizures, catamenial epilepsy), "Sex and Epilepsy" (including sexual function, relationships), "Parenthood with Epilepsy" (including heritability/genetics, post-partum concerns), "Pregnancy with Epilepsy" (including effects of seizures/ASMs during pregnancy), and "Preparing for Pregnancy" (including planning, folic acid, fertility). There was a negligible positive correlation (r=0.05) between importance for ages 14-17 and 18-26. For ages 14-17 years, categories rated at least 4/5 for importance included "Contraception" (4.50/5), "Sex and Epilepsy" (4.32/5), "Hormonal Changes" (4.3/5)," and "Preparing for Pregnancy" (4.12/5). For ages 18-26 years, all categories were rated at least 4/5: "Pregnancy with Epilepsy" (4.64/5), "Preparing for Pregnancy" (4.56/5), "Contraceptives" (4.51/5), "Parenthood with Epilepsy" (4.48/5), "Sex and Epilepsy" (4.47/5), and "Hormonal Changes" (4.21/5).

CONCLUSIONS: PWECP ages 14-26 want counseling about reproductive health and epilepsy from neurologists that is more comprehensive than current AAN recommendations and tailored by age. Disclaimer: Abstracts were not reviewed by Neurology® and do not reflect the views of Neurology® editors or staff. Disclosure: The institution of Dr. Kirkpatrick has received research support from American Epilepsy Society. The institution of Dr. Kirkpatrick has received research support from Child Neurologist Career Development Program. The institution of Dr. Kirkpatrick has received research support from Child Neurology Foundation. Dr. Kirkpatrick has received personal compensation in the range of $500-$4,999 for serving as a Meeting Attendee with One8 Foundation. Dr. Kirkpatrick has received personal compensation in the range of $500-$4,999 for serving as a Meeting Attendee with Brigham and Women's Hospital. Dr. Kirkpatrick has received personal compensation in the range of $0-$499 for serving as a Meeting Attendee with Pediatric Epilepsy Research Consortium. Dr. Kirkpatrick has a non-compensated relationship as a Board of Directors member with My Epilepsy Story that is relevant to AAN interests or activities. Miss Friel has nothing to disclose. Ms. Rivero-Guerra has nothing to disclose. Ms. Tao has nothing to disclose. Dr. Kassiri has nothing to disclose. Dr. Clements has nothing to disclose. The institution of Dr. Briscoe Abath has received research support from Harvard Medical School. Dr. Briscoe Abath has a non-compensated relationship as a Board of Trustees Member with Brother's Brother Foundation that is relevant to AAN interests or activities. Dr. Briscoe Abath has a non-compensated relationship as a Professional Advisory Board with Epilepsy Foundation that is relevant to AAN interests or activities. The institution of Dr. Pennell has received research support from NIH. Dr. Pennell has received publishing royalties from a publication relating to health care. Dr. Burke has nothing to disclose. Dr. Baumann has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant with Projet Jeune Leader (NGO). Dr. Baumann has received personal compensation in the range of $500-$4,999 for serving as a Consultant with Asian Disaster Preparedness Center. The institution of Traci Kazmerski has received research support from Cystic Fibrosis Foundation. The institution of Traci Kazmerski has received research support from NIH.

PMID:40193723 | DOI:10.1212/WNL.0000000000210927

Neurology. 2025 Apr 8;104(7_Supplement_1):2403. doi: 10.1212/WNL.0000000000209122. Epub 2025 Apr 7.

ABSTRACT

OBJECTIVE: To develop and validate a deep learning model to predict time-to-seizure in patients with epilepsy (PWE) from routine EEG.

BACKGROUND: While interictal epileptiform discharges (IEDs) on EEG are associated with higher seizure recurrence, routine EEG has low sensitivity for IEDs and is prone to overinterpretation. Deep learning can extract features from EEG beyond IEDs and map them to complex outcomes, such as seizure risk through time, offering valuable information to guide epilepsy management.

DESIGN/METHODS: We selected all PWE undergoing routine EEG at our institution from 2018-2019, using EEGs recorded after July 2019 as the testing set. Patients with unclear epilepsy diagnoses or seizure during the EEG were excluded. Medical charts were reviewed for the date of the first seizure after the EEG (exact date or extrapolated from seizure frequency) and the date of last follow-up. EEGs were segmented into overlapping 30-second windows and input into a deep transformer model alongside the following clinical features: age, sex, epilepsy type, epilepsy duration, seizure frequency prior to EEG, focal lesion on neuroimaging, family history of epilepsy, and history of febrile seizures. A random survival forest (RSF) using clinical features only was used as a baseline. Models were trained to predict seizure hazards over 18 months at logarithmically spaced intervals and evaluated on the testing set using Uno's concordance index.

RESULTS: We included 504 EEGs from 451 patients for training and 92 EEGs from 83 patients for testing. The deep learning model achieved a concordance index of 0.67, compared to 0.63 for the clinical-only RSF model. Including IEDs as a predictor did not improve the RSF model's performance.

CONCLUSIONS: Deep learning can extract complex information from routine EEG to predict time-to-seizure, outperforming traditional predictors. This suggests a potential role of automated EEG analysis in the follow-up of PWE. Disclaimer: Abstracts were not reviewed by Neurology® and do not reflect the views of Neurology® editors or staff. Disclosure: Dr. Lemoine has received research support from Canadian Institute of Health Research. Dr. Lemoine has received research support from Fonds de Recherche du Québec -- Santé. Dr. Lesage has stock in Labeo Technologies Inc.. Dr. Nguyen has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Paladin Pharma. Dr. Nguyen has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Paladin Pharma. The institution of Prof. Bou Assi has received research support from NSERC. The institution of Prof. Bou Assi has received research support from FRQS. The institution of Prof. Bou Assi has received research support from Brain Canada Foundation . The institution of Prof. Bou Assi has received research support from Epilepsy Canada Foundation. The institution of Prof. Bou Assi has received research support from Savoy Foundation.

PMID:40194014 | DOI:10.1212/WNL.0000000000209122

PLoS One. 2025 Apr 7;20(4):e0318620. doi: 10.1371/journal.pone.0318620. eCollection 2025.

ABSTRACT

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain, leading to distinctive patterns of neuronal dysfunction and the cognitive decline emblematic of dementia. Currently, over 5 million individuals aged 65 and above are living with AD in the United States, a number projected to rise by 2050. Traditional diagnostic methods are fraught with challenges, including low accuracy and a significant propensity for misdiagnosis. In response to these diagnostic challenges, our study develops and evaluates an innovative deep learning (DL) ensemble model that integrates features from three pre-trained models-VGG16, MobileNet, and InceptionResNetV2-for the precise identification of AD markers from MRI scans. This approach aims to overcome the limitations of individual models in handling varying image shapes and textures, thereby improving diagnostic accuracy. The ultimate goal is to support primary radiologists by streamlining the diagnostic process, facilitating early detection, and enabling timely treatment of AD. Upon rigorous evaluation, our ensemble model demonstrated superior performance over contemporary classifiers, achieving a notable accuracy of 97.93%, along with a specificity of 98.04%, sensitivity of 95.89%, precision of 95.94%, and an F1-score of 87.50%. These results not only underscore the efficacy of the ensemble approach but also highlight the potential for DL to revolutionize AD diagnosis, offering a promising pathway to more accurate, early detection and intervention.

PMID:40193955 | DOI:10.1371/journal.pone.0318620

Neurology. 2025 Apr 8;104(7_Supplement_1):1881. doi: 10.1212/WNL.0000000000208805. Epub 2025 Apr 7.

ABSTRACT

OBJECTIVE: To develop and evaluate a deep learning model for predicting brain tumor recurrence following Gamma Knife radiotherapy using multimodal MRI images, radiation therapy details, and clinical parameters.

BACKGROUND: Brain metastases are common, with over 150,000 new cases annually in the U.S. Gamma Knife radiotherapy is a widely used treatment for brain tumors. However, recurrence is a concern, requiring early detection for timely intervention. Previous studies using AI in brain tumor prognosis have primarily focused on glioblastomas, leaving a gap in research regarding metastatic brain tumors post-Gamma Knife therapy. This study aims to address this by developing predictive models for recurrence risk.

DESIGN/METHODS: The study utilized the Brain Tumor Radiotherapy GammaKnife dataset from The Cancer Imaging Archive (TCIA), including MRI images, lesion annotations, and radiation dose details. Data preprocessing involved normalizing MRI images, extracting lesion-specific radiation doses, and applying data augmentation. A 3D Convolutional Neural Network was designed with multiple convolutional layers and trained using stratified sampling. The model was trained for 50 epochs with a batch size of 16 and optimized using the Adam optimizer.

RESULTS: The proof-of-concept model successfully integrated multimodal data and identified stable tumors with accuracy of 79.5% and specificity of 84.4%. However, true negative rates were low indicating difficulty in predicting recurrence. To reduce overfitting, techniques such as augmentation, dropout layers, model checkpoints, and cross validation have been employed to improve generalization. Further steps include feature extraction from complex radiomic profiles to enhance model robustness and accuracy prediction.

CONCLUSIONS: Our study demonstrates the feasibility of using AI to predict brain tumor recurrence post-Gamma Knife radiotherapy. While initial results are promising, further refinement, including the addition of radiomic features and model tuning, is set to improve recurrence prediction and aid in clinical decision-making. Disclaimer: Abstracts were not reviewed by Neurology® and do not reflect the views of Neurology® editors or staff. Disclosure: Mr. Pandya has nothing to disclose. Mr. Patel has nothing to disclose. Miss Anand has nothing to disclose.

PMID:40193918 | DOI:10.1212/WNL.0000000000208805

Neurology. 2025 Apr 8;104(7_Supplement_1):1900. doi: 10.1212/WNL.0000000000208815. Epub 2025 Apr 7.

ABSTRACT

OBJECTIVE: The main aims of this study were to quantify the burden of tau-pathology in an autopsy cohort of clinical cases with cerebral amyloid angiopathy (CAA) and to characterize the presence of perivascular tau (PVT) accumulation and its relationship with CAA.

BACKGROUND: CAA and Alzheimer's Disease Neuropathological Changes, such as tau-tangles, often coexist. The role of tau pathology in the pathophysiology of CAA remains to be determined.

DESIGN/METHODS: Autopsy cases with a neuropathologically confirmed clinical diagnosis of CAA were evaluated. Samples were taken from cortical areas and underwent immunohistochemistry against amyloid-β (Aβ) and phosphorylated tau (At8). Deep-learning based models were created and applied to the samples to quantify 1) density of intraneuronal tau-tangles; 2) percentage area of cortical CAA and Aβ-plaques using the Aiforia® platform; 3) percentage area of total cortical tau-burden, using QuPath. Linear-mixed effects models were applied to assess the association between tau and CAA burden. The presence of dyshoric CAA (flamelike Aβ deposits that radiate into the perivascular neuropil), and PVT (accentuated accumulation of tau around the vessel) were visually identified on Aβ vs. At8-stained sections respectively. Single-vessel analysis was performed to determine whether there was an association between PVT and CAA using Chi-square tests.

RESULTS: A total of 76 sections in 19 CAA cases (median age-at-death 76 years [64-88]; 7 females) were analyzed. Higher tau-tangles and total tau-burden were observed in the temporal cortex versus the occipital cortex (p=0.05). CAA burden was not associated with tau-tangles or total tau-burden. Dyshoric CAA was observed around at least one vessel in 71 (93.4%) of the sections and PVT in 32 (34%) of the sections. In single-vessel analysis, PVT was significantly associated with both dyshoric CAA (p=0.004) and any CAA (p=0.0005).

CONCLUSIONS: Tau was not regionally associated with CAA in this autopsy-cohort. Accumulation of PVT was significantly associated with CAA in the single-vessel analysis. Disclosure: Prof. Farias Da Guarda has nothing to disclose. Ms. vom Eigen has nothing to disclose. Dr. van Harten has nothing to disclose. Ms. Auger has nothing to disclose. Dr. Greenberg has received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Bayer. Dr. Greenberg has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Bristol Myers Squib. The institution of Dr. Greenberg has received personal compensation in the range of $10,000-$49,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Alnylam. Dr. Greenberg has received research support from National Institutes of Health. Dr. Greenberg has received publishing royalties from a publication relating to health care. Dr. Viswanathan has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Alnylam Pharmaceuticals. Dr. Viswanathan has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Biogen. Dr. Viswanathan has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Roche Pharmaceuticals. Dr. van Veluw has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Eisai. The institution of Dr. van Veluw has received research support from NIH. The institution of Dr. van Veluw has received research support from Sanofi. The institution of Dr. van Veluw has received research support from Leducq Foundation. The institution of Dr. van Veluw has received research support from American Heart Association. The institution of Dr. van Veluw has received research support from Frechette Family Foundation. The institution of Dr. van Veluw has received research support from BrightFocus Foundation. The institution of Dr. van Veluw has received research support from Therini Bio. Dr. Perosa has nothing to disclose. Disclaimer: Abstracts were not reviewed by Neurology® and do not reflect the views of Neurology® editors or staff.

PMID:40193903 | DOI:10.1212/WNL.0000000000208815

Neurology. 2025 Apr 8;104(7_Supplement_1):4989. doi: 10.1212/WNL.0000000000212057. Epub 2025 Apr 7.

ABSTRACT

OBJECTIVE: Evaluating the efficacy of 2D and 3D ResNet18-based convolutional neural network models in classifying Parkinson's Disease patients from healthy controls using T1-weighted MRI images.

BACKGROUND: Parkinson's Disease (PD) is a neurodegenerative disorder affecting millions worldwide, with progressive motor and non-motor symptoms. Early diagnosis is critical for optimal management, but current neuroimaging techniques can be complex and time-consuming. Recently, deep learning techniques and advancements have shown potential in automating diagnostic processes. This study aimed to assess the performance of 2D and 3D ResNet18-based convolutional neural network (CNN) models in distinguishing PD patients from healthy controls using T1-weighted magnetic resonance imaging (MRI).

DESIGN/METHODS: We developed two CNN models: a 2D model that utilized mid-sagittal T1-weighted MRI slices, and a 3D model based on volumetric brain data. Preprocessing included data augmentation and transfer learning to enhance model performance. Data were split into 85% for training and 15% for testing, with performance evaluated through accuracy, sensitivity, specificity, and area under the curve (AUC). The models were trained and validated using GPU acceleration for optimized computational efficiency.

RESULTS: The 3D CNN model achieved an accuracy of 94%, outperforming the 2D CNN model, which had an accuracy of 91%. The 3D model also exhibited superior sensitivity (92% vs. 89%) and AUC (0.94 vs. 0.92). Confusion matrices revealed higher specificity and reduced false positives for the 3D model, highlighting its superior diagnostic performance.

CONCLUSIONS: Our results demonstrate that the 3D ResNet18-based CNN significantly outperforms its 2D counterpart in classifying PD patients from T1-weighted MRI images, achieving higher accuracy, sensitivity, and AUC. The superior performance of the 3D model can be attributed to its ability to capture more complex anatomical features, enhancing its diagnostic capability. Further studies should aim to validate the findings across larger, more diverse populations and explore hybrid models that integrate 2D and 3D approaches. Disclaimer: Abstracts were not reviewed by Neurology® and do not reflect the views of Neurology® editors or staff. Disclosure: Dr. Negida has nothing to disclose. Dr. Azzam has nothing to disclose. Dr. Serag has nothing to disclose. Dr. Hassan has nothing to disclose. Rehab Diab has nothing to disclose. Dr. Diab has nothing to disclose. Dr. Hefnawy has nothing to disclose. Mr. Ali has nothing to disclose. Dr. Berman has received personal compensation in the range of $5,000-$9,999 for serving as an officer or member of the Board of Directors for International Parkinson and Movement Disorder Society. The institution of Dr. Berman has received research support from Dystonia Medical Research Foundation. The institution of Dr. Berman has received research support from Administration for Community Living. The institution of Dr. Berman has received research support from The Parkinson Foundation. The institution of Dr. Berman has received research support from National Institutes of Health. Dr. Barrett has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Springer Healthcare LLC. The institution of Dr. Barrett has received research support from Kyowa Kirin. The institution of Dr. Barrett has received research support from NIH.

PMID:40193876 | DOI:10.1212/WNL.0000000000212057

Neurology. 2025 Apr 8;104(7_Supplement_1):4963. doi: 10.1212/WNL.0000000000212039. Epub 2025 Apr 7.

ABSTRACT

OBJECTIVE: To assess the potential of quantitative retinal vascular features as biomarkers of CADASIL.

BACKGROUND: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common inherited cerebral small vessel disease, yet there is currently no biomarker for early detection. Given the anatomical and embryological similarities, the retina has been regarded as a window to cerebral microcirculation. We hypothesized that patients with CADASIL have different quantitative fundoscopic retinal features than matched controls.

DESIGN/METHODS: We conducted a cross-sectional, matched case-control study involving 49 CADASIL cases and 49 age- and sex-matched controls using genetic data from the UK Biobank between 2006 and 2010. Retinal fundoscopic images obtained from the UK Biobank were analyzed using AutoMorph, a deep learning-based tool that measures retinal vascular features, including fractal dimension, tortuosity, and vessel width. Baseline characteristics including age, sex, hypertension, diabetes, smoking status were compared using chi-square or Mann-Whitney-U test appropriately. Wilcoxon signed-rank test or paired-t test was used appropriately to compare these retinal features between cases and controls.

RESULTS: Our analysis included 49 CADASIL cases (mean age of 52.5 ± 7.9, 49% females) and 49 controls (mean age of 52.8 ± 7.9, 49% females). Vascular risk factors including hypertension, diabetes and smoking status were similar between the two groups (p>0.05). No statistically significant differences were observed between CADASIL cases and controls in fractal dimension (p=0.665), average width (p=0.104) or tortuosity measures like distance tortuosity (p=0.423), squared curvature tortuosity (p=0.925) and tortuosity density (p=0.870).

CONCLUSIONS: Quantitative retinal vascular features analyzed in this study did not significantly differentiate CADASIL cases from controls. This could reflect the potential inclusion of CADASIL patients in the early or asymptomatic stages, where retinal changes may not yet be apparent. Furthermore, healthy volunteer bias in the UK Biobank might have influenced these findings. Disclaimer: Abstracts were not reviewed by Neurology® and do not reflect the views of Neurology® editors or staff. Disclosure: Mr. Vallamchetla has nothing to disclose. Dr. badr has nothing to disclose. Mr. Abdelkader has nothing to disclose. Mr. Shourav has nothing to disclose. Xin Li has received personal compensation for serving as an employee of Arizona State University. Yalin Wang has nothing to disclose. The institution of Dr. Meschia has received research support from NINDS. The institution of Dr. Meschia has received research support from NINDS. Dr. Dumitrascu has nothing to disclose. Dr. Lin has nothing to disclose.

PMID:40193854 | DOI:10.1212/WNL.0000000000212039