Robert Edward Hogan

Objective: Quantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on postoperative magnetic resonance imaging (MRI). Current methods for resection cavity masking are time‐consuming and labor‐intensive, and existing automated approaches exhibit variable segmentation accuracy, particularly on extratemporal resections. We developed MELD‐PostOp, a deep learning tool trained and evaluated on a large, heterogeneous cohort to automatically segment resection cavities. Methods: The study included 1.5‐ and 3T postoperative three‐dimensional T1‐weighted MRI images from the Multicentre Epilepsy Lesion Detection (MELD) project ( n _subjects = 969, 27 centers) and from the EPISURG dataset ( n = 133). The cohort included children and adults, alongside a range of resection locations, pathologies, and MRI characteristics. Resection cavities were individually segmented in 285 subjects and used to train an nnU‐Net prototype model. The prototype model was used to generate an additional 680 resection masks, which were subsequently quality‐controlled, edited, and combined with the original 285 to train the final MELD‐PostOp model ( n = 965). A Stratified Test Cohort ( n = 50) and Independent Test Cohort ( n = 87) were withheld for model evaluation. Performance was evaluated using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), number of predicted clusters, and inference runtime, and compared against established tools (Epic‐CHOP, ResectVol, and RESSEG). Results: MELD‐PostOp achieved a median DSC of .85 and HD95 of 3.61 on the combined test cohort, outperforming Epic‐CHOP (DSC .69, HD95 9.67), ResectVol (DSC .66, HD95 15.05), and RESSEG (DSC .43, HD95 32.67), with significant improvements seen in both temporal and especially extratemporal resections. The model detected 98.5% (135/137) of resection cavities. MELD‐PostOp runtime was 17 s per MRI, compared to 612 s (ResectVol), 3205 s (Epic‐CHOP), and 4 s (RESSEG). MELD‐PostOp performance remained high across clinical and imaging subgroups (median DSC > .8). Significance: MELD‐PostOp is an open‐source research tool that provides an accurate, efficient, and generalizable solution for postoperative resection cavity segmentation using only postoperative MRI scans.

Because there are many effective treatments for epileptic seizures, there is a great need for optimal diagnostic and treatment pathways. We present national strategies to address gaps and health equity in patients with epileptic seizures, focusing on efforts in China and the USA. We outline the delay in diagnosis and treatment caused by the “treatment gap.” Efforts in China to address the “treatment gap” include the National Rural Epilepsy Management Project, the establishment of the national epilepsy quality control system, and standardized tertiary epilepsy center development. In the USA, Project Extension for Community Healthcare Outcomes (ECHO) epilepsy enables specialists to partner with primary care clinicians to deliver care. The National Association of Epilepsy Centers facilitates care and contributes to the standardization of presurgical epilepsy evaluation. A major recent and ongoing development is the Epilepsy National Plan in the USA, coordinated through the American Epilepsy Society (AES) with 120 epilepsy-related organizations, to advance a comprehensive national plan that reflects the needs of the entire epilepsy community.

Objective: Quantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on postoperative MRI. Current methods for resection cavity masking are time-consuming and labour-intensive, while existing automated approaches exhibit variable segmentation accuracy, particularly on extra-temporal resections. We developed MELD-PostOp, a deep learning tool trained and evaluated on a large, heterogeneous cohort to automatically segment resection cavities. Methods: The study included 1.5 and 3T postoperative 3D T1-weighted MRI images from the Multicentre Epilepsy Lesion Detection (MELD) project (n _subjects =969, 27 centres) and from the EPISURG dataset (n=133). The cohort included children and adults, alongside a range of resection locations, pathologies, and MRI characteristics. Resection cavities were individually segmented in 285 subjects and used to train an nnU-Net prototype model. The prototype model was used to generate an additional 680 resection masks, which were subsequently quality-controlled, edited and combined with the original 285 to train the final MELD-PostOp model (n=965). A Stratified (STC; n=50) and Independent Test Cohort (ITC; n=87) were withheld for model evaluation. Performance was evaluated using Dice Similarity Coefficient (DSC), 95th percentile Hausdorff distance (HD95), number of predicted clusters and inference runtime; and compared against established tools (Epic-CHOP and ResectVol). Results: MELD-PostOp achieved a median DSC of 0.85 and HD95 of 3.61 on the combined test cohort, outperforming Epic-CHOP (DSC 0.68, HD95 9.54) and ResectVol (DSC 0.66, HD95 12.07), with significant improvements seen in both temporal and especially extra-temporal resections. The model detected 99% (135/137) of resection cavities. MELD-PostOp runtime was 17s per MRI, compared to 612s (ResectVol) and 3205s (Epic-CHOP). MELD-PostOp performance remained high across clinical and imaging subgroups (median DSC > 0.8). Significance: MELD-PostOp provides an accurate, efficient and generalisable solution for postoperative resection cavity segmentation using only postoperative MRI scans. This open-source tool facilitates large-scale quantitative analysis to define what tissue is essential to resect for optimal epilepsy surgical outcomes. Key points: - MELD-PostOp automatically segments resection cavities from a postoperative MRI scan in 17 seconds, a 100-200x faster runtime than existing tools - The model was trained on 965 annotated T1-weighted postoperative scans from sites worldwide, supporting its generalisation across diverse clinical and imaging settings. - The model achieves high segmentation accuracy (DSC > 0.8), with consistently high-quality segmentations across age groups, sex, image resolutions, and resection locations.

Sex differences in drug pharmacokinetics include variations in the expression of the cytochrome P450 enzymes, which are involved in the metabolism of benzodiazepines. It is unclear whether sex influences outcomes associated with intranasally administered drugs. A post hoc analysis of sex differences was conducted to evaluate the effectiveness and safety of diazepam nasal spray, which included examining changes in the number of days between seizure clusters over time (SEIzure interVAL [SEIVAL]). Diazepam nasal spray is approved for acute treatment of seizure clusters in patients with epilepsy aged ≥6 years. Data from a phase 3 safety study were used to determine the proportion of second doses used within 24 h (ie, a proxy for effectiveness) and SEIVAL. Adverse events were recorded. Of 163 treated patients, 89 were female, and 74 were male. Approximately 16% of both sexes self‐administered the study drug. A slightly higher proportion of seizure clusters was treated with a second dose in female (14.7%) than male (9.4%) patients. SEIVAL increased significantly and substantially over a year for all patients. The safety profile was generally similar between the sexes. These results suggest that potential sex differences in benzodiazepine pharmacokinetics do not meaningfully influence outcomes associated with diazepam nasal spray.Plain Language Summary: Some drugs may have differences in absorption and metabolism between genders that could translate into differences in safety and effectiveness. This safety study looked at diazepam nasal spray for treating seizure clusters in patients at least 6 years old. It found that safety was about the same for females and males. For both groups, most clusters stopped after only 1 dose of the drug, and the time between treated clusters got longer over a year.

With more than 6000 attendees between in-person and virtual offerings, the American Epilepsy Society Meeting 2022 in Nashville, felt as busy as in prepandemic times. An ever-growing number of physicians, scientists, and allied health professionals gathered to learn a variety of topics about epilepsy. The program was carefully tailored to meet the needs of professionals with different interests and career stages. This article summarizes the different symposia presented at the meeting. Basic science lectures addressed the primary elements of seizure generation and pathophysiology of epilepsy in different disease states. Scientists congregated to learn about anti-seizure medications, mechanisms of action, and new tools to treat epilepsy including surgery and neurostimulation. Some symposia were also dedicated to discuss epilepsy comorbidities and practical issues regarding epilepsy care. An increasing number of patient advocates discussing their stories were intertwined within scientific activities. Many smaller group sessions targeted more specific topics to encourage member participation, including Special Interest Groups, Investigator, and Skills Workshops. Special lectures included the renown Hoyer and Lombroso, an ILAE/IBE joint session, a spotlight on the impact of Dobbs v. Jackson on reproductive health in epilepsy, and a joint session with the NAEC on coding and reimbursement policies. The hot topics symposium was focused on traumatic brain injury and post-traumatic epilepsy. A balanced collaboration with the industry allowed presentations of the latest pharmaceutical and engineering advances in satellite symposia.