Citations

ABSTRACTObjectiveClinical outcome measures for autoimmune encephalitis (AE) are not yet well defined. Cognitive outcome measures (CogOs) and patient‐reported outcomes (PROs) may capture the symptoms of AE, beyond clinician‐reported outcomes (ClinROs) (the Modified Rankin Scale [mRS] and Clinical Assessment Scale in Autoimmune Encephalitis [CASE]).MethodsThis retrospective cohort study examined LGI‐1‐IgG AE patients at Cleveland Clinic. CogOs included the Rey Auditory Verbal Learning Test (RAVLT) and Brief Visuospatial Memory Test (BVMT). PROs included Neuro‐QoL, PROMIS Global Physical (GPH) and Mental Health (GMH). Abnormal RAVLT, BVMT (≤ −1.5 SD), Neuro‐QoL, and PROMIS (≥ 10 T‐scores) proportions were analyzed. CogOs and PROs were correlated with ClinROs using Spearman's rank correlation.ResultsForty‐four patients (64% male, mean age 68 [SD = 11], median follow‐up 19 months) were included. ClinROs improved, with median mRS decreasing from 2.5 (IQR = 2–3) to 2 (IQR = 1–2), and CASE from 5 (IQR = 3–6) to 3 (IQR = 2–3) (all p < 0.05). RAVLT (n = 15), BVMT (n = 16), Neuro‐QoL (n = 21), and PROMIS (n = 34) were available. At > 12 months from diagnosis, cognition was impaired on BVMT (56%) and RAVLT (20%). PROs showed impairments in Neuro‐QoL lower (38%) and upper (27%) extremity function, cognition (31%), and PROMIS‐GPH (25%). RAVLT correlated with Neuro‐QoL cognition, depression, and stigma, while BVMT correlated with PROMIS‐GMH (all unadjusted p < 0.05). Neither RAVLT nor BVMT correlated with ClinROs. ClinROs correlated with Neuro‐QoL lower extremity function, cognition, and social roles (all p < 0.05).InterpretationLGI‐1‐IgG AE patients have long‐term impairments in cognition, physical health, and social roles, which can be objectively measured using CogOs and PROs and are not captured by ClinROs.

Objective We aimed to understand the impact of Cranial Remolding Orthotic therapy (CRO) on the quality of life of both infants with positional deformational head shape abnormalities (PDHSAs), including plagiocephaly and brachycephaly, and their caregivers. Design Survey study. Setting A single, tertiary academic center. Patients, Participants Families of infants with PDHSAs who underwent CRO between 2007 and 2022. Interventions CRO. Main Outcome Measure(s) Subjective assessment of caregiver perspectives. Results We obtained 242 responses (72% male) from 1013 patients contacted (response rate 23.9%). CRO was initiated at a median age of 6 months (IQR 5-7 months), with a median treatment duration of 4 months (IQR 3-5 months). Insurance covered costs fully for 9.8% of respondents and partially for 47.0%; 43.2% paid completely out-of-pocket. Most were “very satisfied” or “satisfied” with head shape (89.2%) and ear position (93.3%). CRO did not impact children's sleep (75.2%), “never” or “rarely” impacted children's play (90.5%), and never impacted most families’ attendance of social events (91.3%). Hair loss was temporary in 29.9% and permanent in 0.8%. Most families did not find CRO burdensome (73.1%) and “never” or “rarely” felt fatigued (68.5%). Conclusions The study sheds light on the mental and social effects of CRO on patients and caregivers, as well as physical adverse effects associated with treatment. Overall, patients’ families expressed satisfaction with results and felt that administering CRO had minimal negative impacts on themselves and their children.

OBJECTIVE Spinal stereotactic radiosurgery (SSRS) is an established treatment modality for spinal metastatic disease. A common complication of SSRS is postradiation pathologic fracture. The authors evaluated the risk of pathologic fracture in sacral SSRS. METHODS All patients receiving SSRS in 1 to 5 fractions to 1 or more sacral levels between 2011 and 2020 at participating institutions were included. Demographic, histological, anatomical, and outcome data were obtained from electronic medical records. Data were analyzed using SPSS version 28. RESULTS Of 67 total patients, 50 (74.6%) were men and 17 (25.4%) were women. The most common tumor histologies were prostate adenocarcinoma (n = 16 [23.9%]) and renal cell carcinoma (n = 12 [17.9%]). Median (range) age at treatment was 63 (8–87) years. The mean ± SD (range) number of levels treated was 1.5 ± 0.7 (1–4) levels. Mean ± SD (range) bone density at S1 was 208.0 ± 74.4 (−3.0 to 443.3) Hounsfield units (HUs). Median (range) follow-up was 16.6 (1.5–132) months, median ± SD (range) overall survival was 24 ± 21.0 (1.7–83.3) months, and the 1-year local control rate was 83.6%. Fourteen patients (20.9%) experienced fracture within the radiation field. There was no association between fracture and patient sex (p = 0.32), age (p = 0.40), S1 HU (p = 0.28), tumor location according to Denis classification (p = 0.87), presence of pain (p = 0.66), single versus multifraction radiation (p = 0.44), or number of levels treated (p = 0.52). Patients experiencing in-field progression were more likely to experience fracture (61.5% vs 10.2%, p < 0.001), and patients experiencing fracture had a higher average Spine Instability Neoplastic Score than patients without fracture (5.7 vs 4.3, p = 0.049). Only local control remained as a factor associated with sacral fracture in the multivariate analysis. Of the patients experiencing fracture, 10 (71.4%) were symptomatic and 3 of those (30.0%) underwent post-SSRS surgical intervention. CONCLUSIONS Pathologic fracture may occur after sacral SSRS in a significant number of patients and is significantly associated with failure of local control.

Abstract Monotherapy cancer drug response prediction (DRP) models predict the response of a cell line to a given drug. Analyzing these models’ performance includes assessing their ability to predict the response of cell lines to new drugs, i.e. drugs that are not in the training set. Drug-blind prediction displays greatly diminished performance or outright failure across a wide range of model architectures and different large pharmacogenomic datasets. Drug-blind failure is hypothesized to be caused by the relatively limited set of drugs present in these datasets. The time and cost associated with further cell line experiments is significant, and it is impossible to predict beforehand how much data would be enough to overcome drug-blind failure. We must first define how current data contributes to drug-blind failure before attempting to remedy drug-blind failure with further data collection. In this work, we quantify the extent to which drug-blind generalizability relies on mechanistic overlap of drugs between training and testing splits. We first identify that the majority of mixed set DRP model performance can be attributed to drug overfitting, likely inhibiting generalization and preventing accurate analysis. Then, by specifically probing the drug-blind ability of models, we reveal the sources of generalizable drug features are confined to shared mechanisms of action and related pathways. Furthermore, we observed that, for certain mechanisms, we can significantly improve performance by limiting the training of models to a single mechanism compared to training on all drugs simultaneously. We conclude that drug-blind performance is a poor benchmark for DRP as it does not describe model behavior, it describes dataset behavior. Our investigation displays that deep learning models trained on large, monotherapy cell line panels can more accurately describe mechanism of action of drugs rather than their advertised connection to broader cancer biology. Author summary In this paper, we characterize the feature space of cancer drug-blind prediction. To understand the efficacy of a novel cancer drug it has never seen before (drug-blind), a model must be able to accurately compare this drug to all drugs it saw during training. These relationships between cancer drugs, the feature space, must be described well enough that this is possible. We believe that these relationships are poorly defined because cancer DRP models always display reduced performance in a drug-blind context. For the first time, we quantified the limits of generalization in a drug-blind setting. We showed that drug-blind generalization describes mechanistic relationships among drugs during model training. We also outlined new criteria with which to judge the drug-blind ability of a model. Failure of drug-blind prediction is an oft overlooked shortcoming in cancer DRP with potentially damaging downstream implications. We hope to show drug-blind ability of these models in a new light to guide others towards more pertinent tasks in cancer deep learning.

PURPOSE Survivors of adolescent and young adult (AYA)-onset cancers require comprehensive cancer surveillance care. Guidelines recommend 1-4 clinician visits annually for 5 years depending on stage and disease. The goal of this study was to identify factors associated with patterns of post-treatment primary and oncology care in a large cohort of survivors of AYA-onset cancers diagnosed within an integrated health care system. METHODS Patients diagnosed with cancer between 2006 and 2020 age 15-39 years were included. Surveillance visits were identified from electronic medical records 2-5 years after diagnosis. Multivariable logistic regression was used to assess associations with zero oncology specialty visits. RESULTS Of 7,925 survivors, 46% were Hispanic, 6% non-Hispanic Black, 11% non-Hispanic Asian, 35% non-Hispanic White, and 65% female. One-quarter had no oncology specialty visits in the first surveillance year rising to 38% in year 5; 31% had 3+ visits in the first year, declining to 13% in year 5. Over the surveillance period, 17% did not have any oncology specialty visits and 6% had no primary care or oncology visits. Those who were male (odds ratio [OR], 1.21 [95% CI, 1.02 to 1.45]), 20-24 years at diagnosis (OR, 1.58, 95% CI, 1.27 to 1.9; v 35 to 39), or non-Hispanic Black (OR, 1.38, 95% CI, 1.05 to 1.82; v non-Hispanic White) or had high-deductible commercial or Medicaid insurance (OR, 1.35, 95% CI, 1.15 to 1.59; OR, 1.42, 95% CI, 1.11 to 1.82, respectively; v no or low deductible commercial) were more likely to be in the 0-visit group for oncology specialty care. CONCLUSION Although the majority of this cohort received either primary or oncology specialty care, our study identifies those who may need tailored outreach for follow-up care. Continued research into development and testing of interventions to improve uptake of appropriate health care for survivors of AYA-onset cancers is critical.

ABSTRACTIdiopathic multicentric Castleman disease (iMCD) is a rare lymphoproliferative disorder classified into three recognized clinical subtypes—idiopathic plasmacytic lymphadenopathy (IPL), TAFRO, and NOS. Although clinical criteria are available for subtyping, diagnostically challenging cases with overlapping histopathological features highlight the need for an improved classification system integrating clinical and histopathological findings. We aimed to develop an objective histopathological subtyping system for iMCD that closely correlates with the clinical subtypes. Excisional lymph node specimens from 94 Japanese iMCD patients (54 IPL, 28 TAFRO, 12 NOS) were analyzed for five key histopathological parameters: germinal center (GC) status, plasmacytosis, vascularity, hemosiderin deposition, and “whirlpool” vessel formation in GC. Using hierarchical clustering, we visualized subgroups and developed a machine learning‐based decision tree to differentiate the clinical subtypes and validated it in an external cohort of 12 patients with iMCD. Hierarchical cluster analysis separated the IPL and TAFRO cases into mutually exclusive clusters, whereas the NOS cases were interspersed between them. Decision tree modeling identified plasmacytosis, vascularity, and whirlpool vessel formation as key features distinguishing IPL from TAFRO, achieving 91% and 92% accuracy in the training and test sets, respectively. External validation correctly classified all IPL and TAFRO cases, confirming the reproducibility of the system. Our histopathological classification system closely aligns with the clinical subtypes, offering a more precise approach to iMCD subtyping. It may enhance diagnostic accuracy, guide clinical decision‐making for predicting treatment response in challenging cases, and improve patient selection for future research. Further validation of its versatility and clinical utility is required.

SUMMARY Tau aggregates propagate through the brain in a prion-like manner in Alzheimer’s disease (AD) and other tauopathies, but the molecular identity and functional partners of the seeding-competent Tau species remain poorly defined. Here, we present an unbiased proteomic profiling of a high-molecular-weight (HMW) Tau-seed isolated from AD patient brains. We contrast this interactome with that of a biochemically similar, seeding-incompetent HMW-Tau species from age-matched healthy controls. Despite comprising less than 5% of total Tau in the brain, Tau-seed associates with a distinct set of proteins enriched in synaptic, mitochondrial, and vesicle-trafficking functions. Cross-species functional screening in Drosophila and mouse models identifies interactors that modulate Tau toxicity and seeding. Spatially resolved analysis of postmortem AD brains reveals heterogenous co-deposition of these proteins with Tau aggregates, suggesting functionally distinct Tau-seed complexes. Together, this dataset provides a framework for understanding selective Tau-seed toxicity and identifies candidate regulators of Tau propagation with therapeutic potential.