Citations

Objective Lennox–Gastaut syndrome (LGS) is a severe treatment‐resistant epilepsy. Although vagus nerve stimulation (VNS) and deep brain stimulation (DBS) are established therapies, comparative data and the impact of dual neuromodulation remain limited. We compared seizure outcomes across VNS, DBS, and combined DBS + VNS therapy in LGS. Methods This retrospective cohort study at Mayo Clinic (2005–2024) included LGS patients treated with VNS and/or DBS . Seizure frequency was assessed at 6, 12, and 24 months post‐stimulation. Responder was defined as 50% or more seizure reduction. Results Forty patients were included (42% female, median, 150 seizures/month). Eleven patients underwent sequential VNS followed by DBS and contributed data to 2 treatment epochs, yielding 3 treatment groups: VNS alone (n = 29), DBS alone (n = 12), and DBS + VNS (n = 10). At 24 months, median seizure reduction rate was comparable between VNS (50%; 95% confidence interval [ CI], 44–75%) and the overall DBS cohort (59%, 95% CI, 33–86%) ( p = 0.54). However, a significant difference emerged when comparing the 3 treatment arms ( p = 0.027). Specifically, DBS + VNS therapy yielded significantly greater median reduction rate (81%, 95% CI, 60–99%) than DBS alone (43%, 95% CI, 0–58%) ( p adj = 0.027) or VNS alone (50%, 95% CI, 44.4–75.0%) ( p adj = 0.048). Four patients attained sustained seizure freedom above 1 year at last follow‐up. Interpretation VNS and DBS provide comparable seizure reduction in LGS over a 2‐year follow‐up period. DBS + VNS was associated with further improvements in seizure reduction in this small, retrospective dataset. This possibility warrants further investigation. ANN NEUROL 2026

Importance Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used. Objective To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads. Data Sources PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025). Study Selection Studies were included if they provided Centiloids, radiotracer, age, and sex. Data Extraction and Synthesis Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis. Main Outcomes and Measures Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available. Results This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I 2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I 2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I 2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I 2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I 2 = 80%). Conclusions and Relevance In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.

Abstract Genetic studies have significantly advanced our understanding of tauopathies, yet the genetic aetiology of Pick’s disease, a rare 3-Repeat tauopathy, remains unclear. The MAPT p.A152T variant has been identified as a risk factor for Alzheimer’s disease and progressive supranuclear palsy, but its role in Pick’s disease is unknown. In this study, we examined the prevalence of MAPT p.A152T in the largest series of neuropathologically confirmed Pick’s disease cases to date (n = 401). Through genotyping, we identified a single mutation carrier in the Pick’s disease cohort (minor allele frequency = 0.12%). We previously reported MAPT p.A152T at a 0.20% frequency in healthy controls (n = 2456), suggesting that it does not associate with 3-Repeat tauopathy risk. To further investigate the effect of the variant on MAPT transcript expression, we used bulk RNA sequencing in Alzheimer’s disease and progressive supranuclear palsy A152T mutation carriers. We did not detect significant differences in 4-Repeat tau levels, though preliminary trends may indicate more nuanced effects that need to be examined with long-read sequencing in a larger series. Overall, our study suggests that MAPT p.A152T does not increase Pick’s disease risk and may instead be linked to 4-Repeat or mixed tau pathologies, warranting further functional investigation.

Abstract Background The left anterior descending (LAD) coronary artery is a critical organ‐at‐risk in thoracic radiotherapy. In thoracic stereotactic body radiation therapy (SBRT), sharp dose gradients and poor LAD contrast on 4DCT scans introduce uncertainty in LAD dose assessment. Methods We evaluated the performance of an AI‐generated LAD region structure as a surrogate for manual LAD contours in early‐stage NSCLC. We used deep inspiration breath hold (DIBH) scans from 49 patients with manually delineated LADs; planning risk volume contours were expanded by 3 mm. Then, 96 lung SBRT dose maps were registered by matching the left anterior portion of the heart contours. Dice similarity coefficient (DSC), inclusion ratios (IR), dose–volume histogram (DVH) metrics, linear regression, and linear mixed models were used to compare the structures. The correlation of the AI LAD region and LAD dose to mean heart dose (MHD) was assessed. Results The AI LAD region showed strong correlation with manual LAD contours for maximum dose (D1%, R 2 = 0.93) and volume receiving ≥10 Gy (V10Gy, R 2 = 0.81). The planning risk volume also demonstrated high correlation to the AI LAD region. MHD was a poor predictor of LAD dose ( R 2 ≈0.4). Additional dose metric correlations were assessed. Discussion To investigate dose volume effects to the LAD during radiotherapy for stage I NSCLC, a robust segmentation paradigm must be established. It must be easily implementable on typical imaging acquired during treatment, with an emphasis on clinical translatability. Our model has shown that the AI LAD region has a strong correlation with LAD dose under typical lung SBRT doses and supports its use for further investigation into LAD dose volume constraints during lung SBRT. Conclusion AI LAD region provides a clinically feasible and scalable alternative to manual LAD contouring for dose estimation in lung SBRT. Its strong correlation with key dose metrics validates its use in large‐scale retrospective and prospective studies.

PURPOSE Social determinants of health (SDOH) shape cancer treatment access, adherence, and outcomes, yet their collection and use within National Clinical Trials Network (NCTN) studies remain poorly defined. This study examined how demographic and SDOH-related variables were historically collected and published to identify gaps that constrain the study of social context within NCTN trials. METHODS Participant-level data from completed phase III trials for gastrointestinal (GI) cancers conducted by the Alliance for Clinical Trials in Oncology (Alliance) or its legacy groups from 1999 to 2018 were pooled. Thirteen measures were assessed: four demographic and nine SDOH-related. The proportion of individual participants with data collected for each measure was evaluated. Data utilization was evaluated for all primary articles and via targeted review of related publications using the number either reporting subgroups or publishing analyses for each measure. RESULTS A total of 12,701 participants across 11 trials were included. All trials collected demographic measures (<2% of patients missing), insurance status (1% missing), and ZIP code (3% missing). Education level was collected in two trials (28% missing) with marital and employment status collected in one trial (41% missing for both). No trials collected data on other SDOH-related measures. Education level was the only SDOH-related measure reported in primary articles, and none reported analysis of SDOH-related measures. Of the 149 related publications, <2% reported or analyzed any SDOH-related measure. CONCLUSION Across Alliance and NCTN legacy group phase III trials for GI cancers, demographic variables have been consistently collected and frequently reported or analyzed, whereas SDOH-related measures are inconsistently collected and rarely published in the literature. These findings demonstrate a historical absence of routine SDOH data capture and utilization within NCTN trials.

Importance High-quality evidence guiding opioid prescribing decisions for acute pain across common diagnoses is lacking. Objective To describe pain trajectories and patterns of opioid and nonopioid treatment use among individuals who were offered opioids for the treatment of acute pain. Design, Setting, and Participants This was a prospective cohort study of patients recruited from 5 US health systems between September 2020 and March 2023 in emergency departments (EDs), primary care clinics, dental practices, or after cesarean delivery or knee replacement. Eligible patients were opioid-naive adults aged 18 years or older at all study sites, as well as adolescents aged 15 to17 years undergoing impacted molar extraction at 1 site, who were offered an opioid prescription for acute pain. Analysis was conducted April 2023 through February 2026. Exposure Offer of a prescription for an opioid analgesic. Main Outcomes and Measures Time to pain resolution (3 consecutive reports of no pain), patterns of opioid and nonopioid treatment use, and opioid-related adverse effects, ascertained from digital questionnaires. Results Among 1708 enrolled patients (median age, 38 years [IQR, 28-52 years]; 615 [36.0%] reporting race or ethnicity underrepresented in studies of acute pain management) followed up for 180 days, 915 (53.6%) were recruited in EDs, 307 (18.0%) in primary or urgent care, 263 (15.4%) in dental settings, and 223 (13.1%) in inpatient settings. Pain sources included dental (302 patients [17.7%]), trauma or injury (302 [17.7%]), obstetric (176 [10.3%]), musculoskeletal (131 [7.7%]), and low back (100 [5.9%]). Among 1502 patients reporting pain level at least once, median time to pain resolution irrespective of the treatment approach was 20 days (IQR, 8-88 days), with longer durations for surgical pain (74 days [IQR, 30 days to not reached]) and low back pain (69 days [IQR, 18 days to not reached]). The median time to opioid discontinuation among 1189 patients (69.6%) who reported any opioid use was 7 days (IQR, 2-31 days); an estimated 10.0% (95% CI, 7.7%-12.7%) of patients used opioids for at least 90 days, with higher rates in people reporting frequent pain before enrollment. Of 1482 patients (86.8%) completing at least 1 survey during the first 2 weeks of follow-up, 1153 (77.8%) reported using any opioids and 1287 (86.8%) reported using acetaminophen or ibuprofen. Among 619 (52.1%) patients with any opioid use who reported the dose of opioids taken in the first 15 days, daily doses were low (median, 10 [IQR, 5-15] morphine milligram equivalents). Most respondents reported leftover opioids (657 of 982 responding [66.9%]). Conclusions and Relevance In this cohort study of opioid-naive patients with acute pain, opioid use was generally low dose and of short duration, although some patients reported prolonged opioid use; most reported achieving pain resolution within 3 weeks, with longer times for surgical and low back pain. The findings suggest current guidelines for multimodal treatment and for short-duration opioid prescriptions if needed will serve many but not all patients, and treatment should be tailored to address individual patients’ needs.

ABSTRACT This case highlights the diagnostic challenges posed by rare oropharyngeal neurofibromas, their potential to mimic other conditions, and the novel application of transoral robotic surgery (TORS) for the management of an oropharyngeal neurofibroma. A 47‐year‐old female presented with a 1‐year history of a left tonsillar mass. Clinical evaluation, imaging, and biopsy results were inconclusive but raised suspicion for a neoplasm. The patient underwent robotic‐assisted tonsillectomy and mass resection. Subsequent histopathological evaluation confirmed a benign spindle cell tumor consistent with neurofibroma. Oropharyngeal neurofibromas may present diagnostic challenges due to their resemblance to common tonsillar pathologies. Imaging and biopsies may not always provide definitive diagnoses, necessitating surgical excision for accurate characterization. This case demonstrates the utility of TORS, offering improved visualization and access while minimizing patient morbidity.

Abstract Objective Genicular nerve radiofrequency (gRF) treatment is a widely used intervention for chronic pain from knee osteoarthritis (KOA). This systematic review and meta-analysis focuses on RCTs utilizing image guidance to address inconsistencies in previous reviews and provide higher-quality evidence. Design, Setting, and Methods This study included RCTs that compared gRF techniques (conventional, cooled, pulsed, bipolar) to non-gRF treatments, including sham procedures, intra-articular injections, ozone therapy, and oral medications. Primary and secondary outcomes were changes in pain and total WOMAC scores at 3, 6, and 12 months post-gRF treatments. Subgroup analyses explored differences by lesion size, use of prognostic blocks, industry sponsorship, and study location. Statistical heterogeneity was assessed via I 2 statistics. Results Eleven RCTs with 868 knee osteoarthritis patients (435 gRF, 433 non-gRF) were included. At 3 months, gRF significantly reduced pain (SMD -1.65, 95% CI: -3.20 to -0.10, I 2 = 98%, P = 0.040), but not at 6 months or 12 months. Secondary analyses showed no significant differences in total WOMAC scores between the gRF and control groups at 3, 6, and 12 months. Subgroup analysis showed that industry-sponsored studies reported significant 3-month pain reduction, whereas non-sponsored studies did not. This effect was independent of study location. Conclusions GRF provides short-term pain relief (up to 3 months) but neither functional improvement nor sustained pain reduction compared to non-gRF treatments. Our findings do not support the routine use of gRF for long-term chronic pain due to KOA.

Background/Objectives: To develop and evaluate a deep learning-based quality control system for Lumbar Spinal Digital Radiographs (LSDR), designed to automate and improve their evaluation and reduce reliance on manual reviews. Methods: This retrospective study utilized a deep learning workflow comprising image segmentation, feature extraction, and a classification model. The dataset, including anteroposterior (AP) and lateral (LAT) X-ray images, was expanded through data augmentation techniques. Four U-Net-based models were assessed: standard U-Net, Swin-UNet, Attention U-Net, and Attention U-Net with the weight map, with the latter selected for its superior performance. Extracted features, such as brightness, contrast, and anatomical positioning, were used in an XGBoost classifier, which was evaluated using mean intersection over union (mIoU), accuracy, sensitivity, specificity, and AUC. Results: The Attention U-Net with weighted attention outperformed the other models, achieving high mIoU scores in both AP and LAT views. The XGBoost classifier achieved the best performance in classifying images as “qualified” or “unqualified,” with an AUC of approximately 0.9, high accuracy, and balanced sensitivity and specificity. This approach effectively addressed class imbalances and improved model accuracy compared to traditional machine learning models such as MLP and SVM. Conclusions: The developed automated quality control system demonstrated potential for enhancing image quality, enhancing diagnostic reliability, and optimizing clinical workflow efficiency.