Citations

Objective To characterize regional brain atrophy patterns, evaluate their association with regional leucine‐rich glioma‐inactivated‐1 ( LGI1 ) gene expression, and identify clinical predictors of hippocampal atrophy in LGI1 autoimmune encephalitis (AE). Methods This study compared regional brain volumes in LGI1‐AE patients versus age‐ and sex‐matched healthy controls (HC) and Alzheimer's disease (AD) patients. LGI1 gene expression was derived from the Allen Human Brain Atlas. Multivariable linear regression and generalized linear models were utilized. Results A total of 55 LGI1‐AE patients were included. Median age was 68 years (IQR 62.5–74.2 years) at volumetric analysis, after 19 months (IQR 11–45 months) from symptom onset. Compared with HC ( n = 55) and AD patients ( n = 55), LGI1‐AE patients had greater regional atrophy of the hippocampus, insula, caudate, and putamen ( p < 0.002). Longitudinal analyses demonstrated hippocampal and pallidal atrophy after covariate adjustment. Among those who did not fulfill limbic encephalitis criteria and/or had non‐lesional magnetic resonance imaging, limbic and striatal atrophy were identified compared with HC ( p < 0.01). Regions with the greatest differential atrophy corresponded to higher LGI1 gene expression (Spearman ρ = 0.7, p = 0.005). Severe cognitive deficits at diagnosis ( β = −0.56; p = 0.002) and longer disease duration ( β = −0.01; p = 0.001) independently predicted total hippocampal volume ( R 2 = 0.31). Interpretation LGI1‐AE demonstrates a distinct pattern of limbic and striatal atrophy that differs from AD and aligns with regional LGI1 expression, supporting a model of selective network vulnerability. Greater baseline cognitive impairment and longer disease duration independently associated with hippocampal atrophy, underscoring the potential importance of early diagnosis and treatment to limit structural brain injury. ANN NEUROL 2026

Background: Many neurological and cognitive disorders have early oculomotor hallmarks. The Oculo-Cognitive Addition Test (OCAT) is a quick, objective screening tool that measures eye movement and time-based biomarkers of cognitive processing during simple mental addition tasks in under two minutes. Normative values for age, sex, education level, and apolipoprotein E Type 4 (APOE ε4) carrier status must be established so cognitively normal populations are not misdiagnosed. Methods: A total of 382 participants completed OCAT, with 350 participants making up the normative cohort from which raw gaze data were processed to derive fixations, saccades, blinks, pupillary dynamics, and time-based features. Separate multiple linear regression models were fitted for each feature with age, sex, education, and APOE ε4 carrier status as predictors to establish demographic-adjusted normative equations. Results: Processing time, fixation time, fixation size and fixation area and their variability increased progressively from the first to the third number within each addition sequence, consistent with increasing cognitive workload. Demographic-adjusted multiple linear regression models generated normative equations for individual OCAT features based on age, sex, education, and APOE ε4 carrier status. These models were used to derive individualized expected values, 95% reference intervals, and z-scores. Conclusions: These demographic-adjusted normative equations enable individualized interpretation of OCAT performance and are necessary steps towards clinical implementation.

This guideline has been updated. This ASCO Living Guideline is reviewed on an eight-week cycle. Please see the online dynamic version for the latest updates. Living guidelines are developed for selected topic areas with rapidly evolving evidence that drives frequent change in recommended clinical practice. Living guidelines are updated on a regular schedule by a standing expert panel that systematically reviews the health literature on a continuous basis, as described in the ASCO Guidelines Methodology Manual . ASCO Living Guidelines follow the ASCO Conflict of Interest Policy Implementation for Clinical Practice Guidelines . Living Guidelines and updates are not intended to substitute for independent professional judgment of the treating clinician and do not account for individual variation among patients. See appendix for disclaimers and other important information (Appendix I and Appendix II). Updates are published regularly and can be found on the ASCO Publications website .

Objective Hospitalization, particularly for surgery, represents a period of heightened stress. We evaluated how frailty attenuates the association of rheumatoid arthritis (RA) with negative health outcomes after elective noncardiac surgery. Methods We conducted a cross‐sectional study using the National Inpatient Sample (2018 to 2020), including adults (aged ≥18 years) with elective hospitalizations with noncardiac surgery. Frailty was measured using the Hospital Frailty Risk Score (HFRS). Surgeries were categorized by risk level. Outcomes included discharge health resources need (DHRN), composite adverse clinical events (CACE), and death. Results Among 8,800,359 hospitalizations (2.14% among patients with RA), 73.36% underwent intermediate‐risk surgery and 62.25% had HFRS above 0. DHRN occurred in 28.9%, CACE occurred in 0.93%, and all‐cause mortality occurred in 0.17%. A 10‐point increase in HFRS was associated with a 3.8‐fold increased DHRN, 8.5‐fold increased CACE, and 22.8‐fold increased mortality. RA was not independently associated with CACE (adjusted odds ratio [aOR] 0.91; 95% confidence interval [CI] 0.82–1.01; P = 0.08) or death (aOR 0.90; 95% CI 0.71–1.15; P = 0.41) but was significantly associated with DHRN (aOR 1.28, 95% CI 1.25–1.31; P < 0.001) after adjusting for confounders and frailty. In sensitivity analysis, RA had 10% higher relative odds of DHRN following orthopedic (aOR 1.10; 95% CI 1.07–1.13; P < 0.001) but not nonorthopedic intermediate‐risk surgery (aOR 1.10; 95% CI 0.98–1.23; P = 0.11). Conclusion RA was associated with higher odds of DHRN after orthopedic intermediate‐risk surgery but not CACE or inpatient mortality. Moreover, HFRS was associated with DHRN, CACE, and death. These findings underscore the need for disease‐specific clinical considerations and highlight the potential utility of HFRS in discharge planning.

ABSTRACT Background Prior studies in the pre‐MRI era suggest that patients with NCCN Intermediate and High Risk Grade Group 1 prostate cancer (GG1 PCa) on biopsy have increased risk for adverse oncologic outcomes. However, prostate MRI has altered patient selection for biopsy and improved prostate sampling. Herein, we examined factors associated with adverse pathologic features (APF) at radical prostatectomy (RP) and biochemical recurrence (BCR) among patients with GG1 in the MRI era. Methods A prospectively maintained institutional prostate cancer registry identified patients with GG1 PCa on biopsy who underwent RP from 2000 to 2023, stratified by MRI use. Multivariable logistic regression and Cox regression identified factors associated with APF at RP (GG3‐5, ≥ pT3a, or pN1) and BCR free survival (two consecutive PSA ≥ 0.2 ng/mL), respectively. Results Among patients without MRI ( n = 6732), NCCN risk group was associated with APF, BCR, and distant metastasis. Among 302 patients with GG1 and pre‐operative MRI, NCCN risk group was associated with neither APF nor BCR. Age (OR 1.07, 95% CI 1.01–1.14, p = 0.01), PIRADS ( p = 0.01), and % core positivity (OR 1.02, 95% CI 1.00–1.03, p = 0.04) were associated with APF. Age (HR 1.07, 95% CI 1.01–1.13, p = 0.03), APF (HR 4.36, 95% CI 2.14–8.86, p < 0.001), and positive margins (HR 4.88, 95% CI 2.39–9.98, p < 0.001) were associated with BCR. Conclusions Prior to prostate MRI, NCCN risk groups offered meaningful prognostication among patients with GG1 PCa. However, the utility of NCCN risk groups for GG1 in the MRI era appears limited. Contemporary risk stratification for GG1 should focus on biopsy and MRI characteristics rather than NCCN criteria.

ABSTRACT Oral Cancer often occurs from the transformation of precursor lesions, and this offers an opportunity for early detection. Current methods to assess risk of precursor lesion progression to oral cancer incompletely predict risk. A multimodal framework that leverages machine learning is needed to improve prediction. In this manuscript, we propose more standardization in the study of OPMD across sites and propose a framework aligned to the principles of the NCI's Human Tissue Atlas Network (HTAN). We explore whole exome sequencing for genomic risk, methylation array for environmental genetic regulation, RNA sequencing for transcriptional control of cellular programs with spatial transcriptomics to fulfill the spatial requirements of HTAN. Moreover, we propose a machine learning (ML) analysis whereby a learned image‐molecular association is created by integrating H&E histology and sequencing results. Once these associations are learned, a “Teacher‐Student” model can be created whereby the Student model can be deployed to infer the “molecular associations” from H&E and patient covariates alone. Such a framework integrating multiomic data across multiple different biologic processes and time points can help to better understand and potentially even predict the biologic events that underlie oral cancer initiation.

Imaging biomarkers complement traditional risk assessment by enabling direct patient-specific detection and characterization of subclinical atherosclerotic cardiovascular disease, which supports earlier diagnosis, improved risk stratification, and more personalized cardiovascular prevention.