Faculty Bibliography

OBJECTIVES/OBJECTIVE:Large language models (LLMs) are increasingly used in medicine and research, but neuroradiologists' awareness, perceived utility, and concerns about integrity and disclosure remain incompletely characterized. This survey aimed to assess radiologists' awareness and perceptions of LLMs in clinical and research domains. MATERIALS AND METHODS/METHODS:An anonymous, voluntary SurveyMonkey survey was distributed internationally (October 1, 2024 to March 31, 2025) via neuroradiology society newsletters/membership channels and social media. Categorical variables were summarized as counts and percentages; Likert items were summarized using weighted means and response distributions. Item-level complete-case denominators were reported. Prespecified subgroup analyses used chi-square/Fisher exact tests (categorical) and nonparametric tests (ordinal), with Holm multiplicity control within prespecified multi-item question blocks and within each subgroup factor. RESULTS:A total of 265 respondents started the survey; after exclusions, 209 were included in the analytic sample, of whom 64.6% were male. Awareness of LLMs was high (ChatGPT: 83.3%), but knowledge gaps persisted (14.8% unfamiliar with all listed models; 16.7% misclassified DALL·E as an LLM). Respondents most frequently endorsed bounded, workflow-adjacent clinical applications, including guideline-based recommendations (75.6%) and protocol selection (60.8%), with lower endorsement for image interpretation (22.0%). Concerns were common regarding plagiarism/data fabrication (82.1%), inaccurate or biased outputs (75.8%), and accountability (72.1%), alongside support for AI-detection tools (77.0%) and documentation of LLM use aligned with an example journal policy (73.1%). CONCLUSION/CONCLUSIONS:Survey respondents reported cautious optimism toward LLM integration, favoring workflow-adjacent applications while emphasizing disclosure, oversight, and targeted education.

BACKGROUND:Posterior circulation (PC) large vessel occlusions (PC-LVO) include occlusions of vertebral arteries, basilar artery and P1 segment of posterior cerebral artery, although the latter is variably classified as a medium vessel occlusion in some studies and clinical trials. PC-LVOs account for a substantial proportion of PC strokes. PC-LVO remains challenging to diagnose due to nonspecific symptoms and limited sensitivity of CT for posterior fossa ischemic changes. However, PC-LVO is associated with substantial disability and mortality, particularly with basilar artery occlusion (BAO). Recent randomized trials demonstrate a clear benefit of endovascular thrombectomy (EVT) for BAO up to 24 hours in selected patients, shifting treatment paradigms toward imaging-guided selection. METHODS:A working group comprising select members of the Executive Committee and other members of the American Society of Functional Neuroradiology convened to develop a clinical State-of-Practice document on neuroimaging in acute PC-LVO stroke. The aim of this document is to provide a concise, practical, and evidence-informed imaging framework for the diagnosis, triage, and prognostication of patients with PC-LVO stroke in everyday neuroradiology practice. KEY POINTS/CONCLUSIONS:A multimodal imaging approach is necessary for PC-LVO stroke detection. NCCT is relatively insensitive for ischemia detection but remains essential for excluding hemorrhage, while CTA serves as the diagnostic cornerstone for identifying PC-LVO. CT perfusion may provide additional information, although its sensitivity in the posterior fossa is limited. DWI-MRI remains the reference standard, despite the occurrence of early false negatives and the limited reliability of DWI-FLAIR mismatch in posterior circulation stroke. Imaging biomarkers, including PC-ASPECTS and brainstem infarct scores, are important for patient selection and outcome prediction.

BACKGROUND:The therapeutic landscape of acute ischemic stroke (AIS) has been transformed by expanding endovascular therapy (EVT) criteria. With the incorporation of MR perfusion imaging in the 2026 AHA/ASA Guidelines for the early management of patients with AIS to support EVT patient selection, there is renewed energy and focus on the clinical applications of different MR perfusion techniques in AIS. A reliable, contrast-free perfusion technique with acceptable acquisition time and capability to identify salvageable tissue and assess collateral status may be utilized in certain clinical contexts. Arterial spin labeling (ASL) is a clinically feasible technique that can be considered as a valuable modality within stroke workflows and MRI-based EVT selection protocols, particularly in patients with renal insufficiency, contrast allergy, and contrast-limited settings. Although ASL has been investigated in multiple previous studies, its systematic integration into contemporary acute stroke clinical workflows has yet to be routinely adopted and standardized practical guidance in this regard is lacking. Furthermore, ASL is not yet validated in prospective EVT-selection trials to support timely reperfusion decisions. METHODS AND PURPOSE/UNASSIGNED:This state-of-practice paper was developed on behalf of the American Society of Functional Neuroradiology (ASFNR) by an expert panel of neuroradiologists with expertise in cerebrovascular imaging and MR perfusion. We appraise the current evidence, clinical applications, implications for therapeutic decision-making, and translational barriers of ASL in AIS triage and EVT patient selection. We also discuss ASL challenges related to workforce capacity and reimbursement, and propose recommendations for future clinical validation and implementation. CONCLUSION/CONCLUSIONS:ASL may serve as a valuable adjunct within MRI-based stroke workflows by providing complementary information on ischemic penumbra and collateral status. However, validation through multicenter prospective studies for EVT patient selection, standardized acquisition protocols, and automated postprocessing quantification pipelines are needed before ASL can be routinely integrated into time-sensitive stroke workflows.

BACKGROUND:Distal medium vessel occlusions (DMVO) constitute approximately 25%-40% of acute ischemic stroke. These potentially disabling strokes remain diagnostically challenging due to vessel caliber, tortuosity, and low sensitivity of standard CT angiography. RECENT DEVELOPMENTS/BACKGROUND:in the MT arm. PURPOSE/OBJECTIVE:Summarize current evidence and provide a "state of practice" guide for radiologists on DMVO detection, workflow standardization, triage, and imaging-based prognostication. KEY POINTS/CONCLUSIONS:Optimized CTA (including multiphase), CT Perfusion (territorial Tmax), and MRI DWI/SWI improve diagnostic confidence for DMVO. Certain perfusion parameters indicative of collateral status, for instance, rCBV index and hypoperfusion intensity ratio, have prognostic value. Structured reporting of important positive and negative radiologic findings can guide neurologic triage despite neutral trials. CONCLUSION/CONCLUSIONS:Radiologists play a central role in DMVO diagnosis and prognostication. Standardized imaging workflows are essential in the post-trial landscape.

BACKGROUND AND PURPOSE/OBJECTIVE:IDH mutation & 1p/19q codeletion are critical biomarkers for glioma diagnosis & therapy. 1p/19q codeletion occurs exclusively in IDH-mutated gliomas. In this study, we developed a 2-stage, non-invasive, MRI-based deep learning method that leverages IDH status to enhance 1p/19q predictions. MATERIALS AND METHODS/METHODS:Predicted IDH-wildtype cases default to 1p/19q non-codeleted. Then the IDH-mutated cases were further classified for 1p/19q status using the 1p/19q-networks. RESULTS: CONCLUSIONS:to gate 1p/19q predictions. The developed method offers a reliable, non-invasive approach to determine important biomarkers for glioma diagnosis.

BACKGROUND AND PURPOSE/OBJECTIVE:-wt GBMs to help radiologists in differentiating them from nonmalignant mimic diagnoses (eg, encephalitis). Additionally, the histologic, genomic, and survival profiles of nonenhancing GBMs were compared with those of enhancing GBMs. MATERIALS AND METHODS/METHODS:-wt GBMs with nonmalignant mimics. Histopathologic and genomic analyses were performed on institutional cases. Overall survival between nonenhancing and enhancing GBMs was compared using Kaplan-Meier analysis. RESULTS:= .078). CONCLUSIONS:Nonenhancing GBMs demonstrate distinct MRI features that must be recognized for early diagnosis and differentiation from nonmalignant mimics. Nonenhancing GBMs demonstrated longer overall survival compared with enhancing GBMs, though they were not statistically significant.

PURPOSE:Glioblastoma IDH-wild type, CNS WHO grade 4 (GBM) can be diagnosed on the basis of histologic features (histological-GBM) or molecular features (molecular-GBM). Only few studies report neuroimaging features of GBM in its modern classification, and none have controlled for surgical status or used multiple logistic regression analysis to determine unique predictors. Our study aimed to validate MRI features that distinguish histological-GBM and molecular-GBM. METHODS: = 44) of GBM cases, classified according to the 2021 WHO Classification of Tumors of the CNS. For the training cohort, univariate and multiple logistic regression analyses determined if MRI metrics (contrast enhancement, ring-enhancement, vasogenic edema, multifocal tumor, lesion diameter, hemorrhage, number of lobes, and normalized ADC) and surgery type (biopsy vs. resection) predicted GBM-type (histological vs. molecular). A reduced multiple logistic regression model was constructed and applied to the validation dataset. RESULTS: = 0.039) differed between histological and molecular-GBM. Analysis of the validation dataset using the unique training dataset-derived predictor variables (contrast-enhancement, ring-enhancement, and normalized ADC) found correct classification of each histological and molecular-GBM. CONCLUSION:Molecular and histological-GBM exhibit distinct MRI phenotypes independent of surgical status. SUPPLEMENTARY INFORMATION:The online version contains supplementary material available at 10.1007/s11060-026-05431-8.

PURPOSE/OBJECTIVE:Histopathological grading of IDH-mutant astrocytomas demonstrates limited prognostic accuracy. However, DNA methylation subclassification has demonstrated improved prognostication beyond histological grading. This study aimed to investigate the associations between imaging features, tumor volumetric data, and DNA methylation grade in IDH-mutant astrocytomas. METHODS:We analyzed imaging features and volumetric data for 72 patients diagnosed with IDH-mutant astrocytomas, who underwent preoperative MRI and DNA methylation profiling. VASARI features and multicompartmental volumetrics were evaluated. Logistic regression was used to identify imaging predictors of methylation subclass, WHO histologic grade, copy number variation (CNV), and CDKN2A/B homozygous deletion. Univariable and multivariable Cox proportional hazard models were also developed to assess these variables' influence on overall survival and progression-free survival. RESULTS:Patients were classified into 27 methylation high-grade (A_IDH_HG) and 45 methylation low-grade (A_IDH_LG) tumors. Tumor volumes and proportions varied by methylation grade, CNV status, and WHO histologic grade, but not by CDKN2A/B status. Imaging features distinguished methylation subclasses with 75% accuracy (AUC = 0.77). Methylation high-grade subclass was associated with imaging features such as midline crossing, ependymal extension, and poorly defined enhancing margins. Predictive performance for WHO histologic grade, CNV status, and CDKN2A/B deletion was moderate (AUC = 0.67, 0.69, and 0.65, respectively). Methylation grade, CDKN2A/B status, VASARI features, and proportions of edema and non-contrast enhancing tumor were significantly associated with survival. CONCLUSION/CONCLUSIONS:MRI-derived imaging features facilitate noninvasive prediction of DNA methylation subclass in IDH-mutant astrocytomas.

Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in brain gliomas. Accurate non-invasive determination of IDH mutation status is crucial for effective therapy and prognosis. However, the variability in imaging protocols across institutions hinders the reliability of deep learning (DL) models used for IDH classification. To address data heterogeneity, a StyleTransfer-GAN (