Faculty Bibliography

PURPOSE/OBJECTIVE:To describe the multimodal imaging findings of the angular sign of Henle fiber layer (HFL) hyperreflectivity (ASHH) at baseline and follow-up in patients with contusion maculopathy. METHODS:Eleven eyes of ten patients were captured with multimodal imaging after non-penetrating ocular blunt trauma from a soccer ball, fist, or airsoft pellet. Baseline clinical and imaging characteristics and follow-up outcomes are presented. RESULTS:Hyper-reflective lesions extending along the HFL from the ellipsoid zone (EZ) to the outer plexiform layer consistent with ASHH were identified with optical coherence tomography (OCT). Mean presenting visual acuity (VA) was logMAR 0.59 ± 0.64 (Snellen VA 20/77, range 20/25 to counting fingers) and follow-up visual acuity was logMAR 0.43 ± 0.35 (Snellen VA 20/53, range 20/20 to 20/200). Additional OCT findings included external limiting membrane attenuation and retinal pigment epithelium (RPE) disruption. On follow-up, resolution of ASHH was accompanied by outer nuclear layer thinning with varying degrees of EZ attenuation and RPE loss. A macular hole was detected in one patient on follow-up. CONCLUSION/CONCLUSIONS:ASHH is a distinctive acute OCT feature of contusion maculopathy secondary to blunt injury, causing disruption of the photoreceptors and presumably anterograde alterations in the HFL. Associated RPE alterations may ensue, either acutely or delayed, and are a biomarker of persistent structural abnormalities and variable visual outcomes.

This article provides a focused update to the clinical practice guideline on the treatment and management of patients with coronavirus disease 2019 (COVID-19), developed by the Infectious Diseases Society of America. The guideline panel presents a recommendation on the use of infliximab in hospitalized adults with severe or critical COVID-19. The recommendation is based on evidence derived from a systematic literature review and adheres to a standardized methodology for rating the certainty of evidence and strength of recommendation according to the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach.

Cytomolecular features critical for risk-stratified treatment determination in pediatric acute myeloid leukemia (AML) were expanded in Children's Oncology Group (COG) Phase III trial AAML1831 based on previous trials. It remains unknown whether the cytomolecular risk profiles are generalizable to the real-world. We addressed this knowledge gap using a nationally representative real-world cohort of 913 pediatric AML patients. Distributions of cytomolecular risk profiles and individual markers were comparable for trial-enrolled and non-enrolled patients, as well as across social drivers of trial enrollment (race/ethnicity, language, insurance, acuity). Compared to patients with only favorable cytomolecular markers (4-year OS 89.48%; 95% CI: 84.46%-92.95%), patients with both favorable and unfavorable (hazards ratio [HR] = 2.49, 95% CI : 1.18-5.23), neutral (HR = 4.33, 95% CI : 2.75-6.82), and only unfavorable (HR = 5.80, 95% CI: 3.70-9.11) markers all had increased hazards of death. Cytomolecular risk informed by trial data appears to be generalizable to the real-world setting in pediatric AML.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Bone metastases, affecting more than 4.8% of patients with cancer annually, and particularly spinal metastases require urgent intervention to prevent neurological complications. However, the current process of manually reviewing radiological reports leads to potential delays in specialist referrals. We hypothesized that natural language processing (NLP) review of routine radiology reports could automate the referral process for timely multidisciplinary care of spinal metastases. METHODS:We assessed 3 NLP models-a rule-based regular expression (RegEx) model, GPT-4, and a specialized Bidirectional Encoder Representations from Transformers (BERT) model (NYUTron)-for automated detection and referral of bone metastases. Study inclusion criteria targeted patients with active cancer diagnoses who underwent advanced imaging (computed tomography, MRI, or positron emission tomography) without previous specialist referral. We defined 2 separate tasks: task of identifying clinically significant bone metastatic terms (lexical detection), and identifying cases needing a specialist follow-up (clinical referral). Models were developed using 3754 hand-labeled advanced imaging studies in 2 phases: phase 1 focused on spine metastases, and phase 2 generalized to bone metastases. Standard McRae's line performance metrics were evaluated and compared across all stages and tasks. RESULTS:In the lexical detection, a simple RegEx achieved the highest performance (sensitivity 98.4%, specificity 97.6%, F1 = 0.965), followed by NYUTron (sensitivity 96.8%, specificity 89.9%, and F1 = 0.787). For the clinical referral task, RegEx also demonstrated superior performance (sensitivity 92.3%, specificity 87.5%, and F1 = 0.936), followed by a fine-tuned NYUTron model (sensitivity 90.0%, specificity 66.7%, and F1 = 0.750). CONCLUSION/CONCLUSIONS:An NLP-based automated referral system can accurately identify patients with bone metastases requiring specialist evaluation. A simple RegEx model excels in syntax-based identification and expert-informed rule generation for efficient referral patient recommendation in comparison with advanced NLP models. This system could significantly reduce missed follow-ups and enhance timely intervention for patients with bone metastases.

High-quality bladder MRI is essential for reliable detection and characterization of bladder tumors. While the Vesical Imaging Reporting and Data System (VI-RADS) was developed to standardize acquisition, interpretation, and reporting of MRI for bladder cancer staging, no universal scoring system has been established for quality assessment. A systematic review showed that existing efforts for bladder MRI quality assessment have largely relied on study-specific, subjective Likert-type scales, with substantial variability, hindering reproducibility and comparability across studies and institutions. In this paper, we describe a novel bladder MRI scoring system that was developed by the multidisciplinary American College of Radiology VI-RADS Steering Committee, the VI-RADS Quality Score. It provides stratification of quality scores-inadequate, adequate, and optimal-based on signal-to-noise ratio, ability to clearly visualize relevant anatomic landmarks, and assessments of common imaging artifacts, which can guide clinical decision-making. It also emphasizes per-sequence scores in addition to an overall assessment to support targeted MRI protocol optimization. The VI-RADS Quality Score will need to be prospectively validated and will facilitate reproducibility across institutions, build trust in bladder MRI within multidisciplinary teams, and accelerate widespread adoption of bladder MRI for bladder cancer imaging. KEY POINTS: Question While high-quality bladder MRI is essential for evaluating bladder cancer, no universal scoring system is established for quality assessment. Findings A systematic review showed that studies on bladder MRI quality had significant variability. A structured scoring system was developed by the multidisciplinary VI-RADS steering committee. Clinical relevance High-quality bladder MRI is essential for reliable evaluation of bladder cancer. The VI-RADS Steering Committee developed a scoring system for bladder MRI quality assessment. This is expected to facilitate reproducibility and build trust in bladder MRI within multidisciplinary teams.