Citations

ObjectiveThis study assessed the relationship between speech and language impairment and outcome in a multicenter cohort of isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD).MethodsPatients with iRBD from 7 centers speaking Czech, English, German, French, and Italian languages underwent a detailed speech assessment at baseline. Story‐tale narratives were transcribed and linguistically annotated using fully automated methods based on automatic speech recognition and natural language processing algorithms, leading to the 3 distinctive linguistic and 2 acoustic patterns of language deterioration and associated composite indexes of their overall severity. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow‐up. The Cox proportional hazard was performed to evaluate the predictive value of language patterns for phenoconversion over a follow‐up period of 5 years.ResultsOf 180 patients free of parkinsonism or dementia, 156 provided follow‐up information. After a mean follow‐up of 2.7 years, 42 (26.9%) patients developed neurodegenerative disease. Patients with higher severity of linguistic abnormalities (hazard ratio [HR = 2.35]) and acoustic abnormalities (HR = 1.92) were more likely to develop a defined neurodegenerative disease, with converters having lower content richness (HR = 1.74), slower articulation rate (HR = 1.58), and prolonged pauses (HR = 1.46). Dementia‐first (n = 16) and parkinsonism‐first with mild cognitive impairment (n = 9) converters had higher severity of linguistic abnormalities than parkinsonism‐first with normal cognition converters (n = 17).InterpretationAutomated language analysis might provide a predictor of phenoconversion from iRBD into synucleinopathy subtypes with cognitive impairment, and thus can be used to stratify patients for neuroprotective trials. ANN NEUROL 2024;95:530–543

Abstract PURPOSE For large brain metastases, it is unknown whether single fraction stereotactic radiosurgery (sSRS) or multi-fraction stereotactic radiosurgery (fSRS) is optimal. We aimed to compare rates of local progression and necrosis between sSRS and fSRS for large brain metastases. METHODS Patients with intact or resected brain metastases with a target volume ≥ 4.2 cc (corresponding to a diameter ≥ 2 cm) treated with Gamma Knife (GK) or linear accelerator-based SRS from 2014-2022 were included in this retrospective review. RESULTS A total of 343 large brain metastases in 276 patients were included; 171 lesions were treated with sSRS (96% with GK) and 172 lesions with fSRS (100% with linear accelerator). Median lesion volume was 19.8 cc and 8.4 cc for fSRS and sSRS, respectively. Resection was performed prior to SRS in 228 (66%), with the remaining 115 metastases treated intact. There was no difference in rate of local progression between sSRS versus fSRS, or between resected versus intact targets (p >0.8 for both). Cumulative incidence of local progression (with death as a competing risk) at 1 and 3 years for patients receiving sSRS was 24% and 30%, respectively, compared to 22% and 34% for fSRS. FSRS was associated with significantly higher rates of necrosis than sSRS (HR 5.58, 2.38-13.08, p < 0.001). This association remained significant after controlling for target volume. Cumulative incidence of necrosis (with death as a competing risk) at 1 and 3 years for patients receiving sSRS was 1% and 3%, respectively, compared to 10% and 18% for fSRS. Necrosis rates were significantly higher with fSRS for both intact and resected targets (P < 0.02 for both). CONCLUSION While local control for large resected or intact brain metastases appears similar after sSRS or fSRS, this retrospective study shows higher rates of necrosis after fSRS. Prospective validation is needed.

Abstract Identification of key phenotypic regions such as necrosis, contrast enhancement, and edema on magnetic resonance imaging (MRI) is important for understanding disease evolution and treatment response in patients with glioma. Manual delineation is time intensive and not feasible for a clinical workflow. Automating phenotypic region segmentation overcomes many issues with manual segmentation, however, current glioma segmentation datasets focus on pre-treatment, diagnostic scans, where treat-ment effects and surgical cavities are not present. Thus, existing automatic segmenta-tion models are not applicable to post-treatment imaging that is used for longitudinal evaluation of care. Here, we present a comparison of three-dimensional convolutional neural networks (nnU-Net architecture) trained on large temporally defined pre-treatment, post-treatment, and mixed cohorts. We used a total of 1563 imaging timepoints from 854 patients curated from 13 different institutions as well as diverse public data sets to understand the capabilities and limitations of automatic segmenta-tion on glioma images with different phenotypic and treatment appearance. We as-sessed the performance of models using Dice coefficients on test cases from each group comparing predictions with manual segmentations generated by trained techni-cians. We demonstrate that training a combined model can be as effective as models trained on just one temporal group. The results highlight the importance of a diverse training set, that includes images from the course of disease and with effects from treatment, in the creation of a model that can accurately segment glioma MRIs at multiple treatment time points.

Abstract Image-based mathematical modeling is emerging as an advantageous tool to integrate into the care routine for patients with brain tumors. These tools can be used for a variety of tasks including predicting tumor growth, indicating spatial genomic alterations, and guiding radiation therapy. Often, the methods to produce these maps are developed in silos, using cohorts of retrospective patients and switching to a per patient map calculation can be an afterthought. Automatic pipelines are needed to enable creating mathematical maps in a clinically-relevant workflow. To this end, we developed a pipeline to create an image-based mathematical map of edema abundance based on T2-weighted (T2W) magnetic resonance imaging that includes preprocessing steps, abnormality and tissue type segmentation, brain extraction, map generation, and conversion between image file formats. The python-based pipeline was designed to take in T1-weighted (T1W), T1W with gadolinium contrast (T1Gd), T2W, and fluid attenuated inversion recovery (FLAIR) DICOM images for a single patient as input and convert them to NIfTIs for downstream steps. The pipeline preprocessed images by correcting bias field fluctuations using the N4 algorithm and normalizing intensities. The T1W, T1Gd, and FLAIR are then registered to the T2W. T1Gd and FLAIR are used in a deep learning algorithm to delineate the brain and abnormality including enhancing tumor, necrosis, and peripheral edema. Normal tissue segmentations are performed using the statistical parametric mapping (SPM) segmentation routine. The segmentations and preprocessed images are all utilized in the edema mathematical model. The resulting edema map is then reformatted as a DICOM and output for further use by clinicians and clinical imaging systems. Our future goals include integrating our pipeline in a clinical workstation and sending final maps to our hospital’s picture archiving and communication system (PACS) to allow for deployment of these models for patient care in a clinically efficient timeline.

Abstract “Scientific writing is a mandatory skill for most researchers, and orthoapedic surgeons are no exception. However, improvement of the writing quality could be achieved using some assistive tools. The most recent ones involve using artificial intelligence (AI), specifically ChatGPT. Although it seems appealing for improving scientific writing and assisting non-native English speakers, its usage is still not well regulated, and ethical issues were raised regarding the misuse of these tools in the scientific research and writing process. Strict regulations controlling the involvement of these tools are still needed, and their judicious and honest utilization is mainly dependent on the researchers themselves.”

The current study evaluated work- and client-related burnout, perceived stress, and race-based stress with a focus on Black, Indigenous, or Persons of Color (BIPOC) mental health providers. An online survey ( n = 82) was distributed in the United States between July 17, 2020, and November 5, 2020. Demographic variables, racial discrimination during coronavirus disease 2019 (COVID-19), perceived stress, and the coping variables were assessed. The majority of respondents were women (85%), BIPOC identifying (72%), aged 25 to 34 years (43%), psychologists (50%), and had been practicing for 9 years. In total, 68% and 27% of providers reported moderate to severe work- and client-related burnout, respectively. Another 87% reported moderate to high stress. Factors associated with a higher level of burnout include being younger, having fewer years of experience, and being a social worker or family therapist. Among BIPOC providers, greater experiences of racial discrimination were associated with higher work-related burnout and stress. Greater use of maladaptive coping was associated with higher burnout and stress, yet we found no associations with the use of adaptive coping. Racial discrimination during COVID-19 was significantly associated with work-related burnout and perceived stress within a sample of predominantly BIPOC mental health providers. These findings reinforce the need for more focused support for mental health providers to ensure a healthy and resilient future workforce.

Basal cell carcinoma (BCC) is the most common form of skin cancer in the United States. Due to the high frequency, BCC occurrences are not typically recorded, and annual rates of incidence can only be estimated. Current estimated rates are 2 million Americans affected annually, and this continues to rise. Exposure to radiation, from either sunlight or previous medical therapy, is a key player in BCC development. BCC is not as aggressive as other skin cancers because it is less likely to metastasize. However, surgery and radiation are prevalent treatment options, therefore disfigurement and limitation of function are significant considerations. The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) outline an updated risk stratification and treatment options available for BCC.

AbstractContextEarly inflammatory thyroid eye disease (TED) can lead to symptomatic chronic disease, including disabling proptosis. Teprotumumab, an insulin-like growth factor-1 receptor (IGF-1R) inhibitor, previously demonstrated efficacy in acute, high-inflammation TED trials.ObjectiveWe present data from the first placebo-controlled trial with teprotumumab in chronic/low disease activity TED.MethodsThis randomized double-masked, placebo-controlled trial, conducted at 11 US centers, enrolled adult participants with TED duration of 2 to 10 years, Clinical Activity Score (CAS) ≤ 1 or no additional inflammation or progression in proptosis/diplopia for ≥1 year, proptosis ≥3 mm from before TED and/or from normal, euthyroid/mildly hypo/hyperthyroid, no prior teprotumumab, and no steroids within 3 weeks of baseline. Patients received (2:1) intravenous teprotumumab or placebo once every 3 weeks (total 8 infusions). The primary endpoint was proptosis (mm) improvement at Week 24. Adverse events (AEs) were assessed.ResultsA total of 62 (42 teprotumumab and 20 placebo) patients were randomized. At Week 24, least squares mean (SE) proptosis improvement was greater with teprotumumab (−2.41 [0.228]) than with placebo (−0.92 [0.323]), difference −1.48 (95% CI −2.28, −0.69; P = .0004). Proportions of patients with AEs were similar between groups. Hyperglycemia was reported in 6 (15%) vs 2 (10%) and hearing impairment in 9 (22%) vs 2 (10%) with teprotumumab and placebo, respectively. AEs led to discontinuation in 1 teprotumumab (left ear conductive hearing loss with congenital anomaly) and 1 placebo patient (infusion-related). There were no deaths.ConclusionTeprotumumab significantly improved proptosis vs placebo in longstanding/low inflammation TED, demonstrating efficacy regardless of disease duration/activity. The safety profile was comparable to that previously reported.