Citations

Abstract Transgender and gender-diverse (TGD) individuals face unique challenges in accessing affirming, evidence-based breast imaging care because of longstanding disparities in provider education, institutional practices, and imaging guidelines. Gender-affirming hormone therapy and gender-affirming surgeries, such as chest masculinization or breast augmentation, impact breast tissue composition and cancer risk, necessitating individualized imaging approaches. Through multiple clinical vignettes, this article illustrates potential pitfalls and key considerations in breast imaging for TGD patients, including appropriate imaging recommendations, inclusive system-level changes, and navigating patient encounters. Education of breast imaging radiologists, technologists, and front-desk staff is critical to bridge the known knowledge gap and improve outcomes for this patient population. By adopting inclusive, culturally competent, and trauma-informed practices, breast imaging professionals can help reduce disparities and improve the experience and health outcomes of TGD patients.

Abstract Background Acupuncture, a key modality in traditional Chinese medicine, is gaining global recognition as a complementary therapy and a subject of increasing scientific interest. However, fragmented and unstructured acupuncture knowledge spread across diverse sources poses challenges for semantic retrieval, reasoning, and in-depth analysis. To address this gap, we developed AcuKG, a comprehensive knowledge graph that systematically organizes acupuncture-related knowledge to support sharing, discovery, and artificial intelligence–driven innovation in the field. Methods AcuKG integrates data from multiple sources, including online resources, guidelines, PubMed literature, ClinicalTrials.gov, and multiple ontologies (SNOMED CT, UBERON, and MeSH). We employed entity recognition, relation extraction, and ontology mapping to establish AcuKG, with human-in-the-loop to ensure data quality. Two cases evaluated AcuKG’s usability: (1) how AcuKG advances acupuncture research for obesity and (2) how AcuKG enhances large language model (LLM) application on acupuncture question-answering. Results AcuKG comprises 1839 entities and 11 527 relations, mapped to 1836 standard concepts in 3 ontologies. Two use cases demonstrated AcuKG’s effectiveness and potential in advancing acupuncture research and supporting LLM applications. In the obesity use case, AcuKG identified highly relevant acupoints (eg, ST25, ST36) and uncovered novel research insights based on evidence from clinical trials and literature. When applied to LLMs in answering acupuncture-related questions, integrating AcuKG with GPT-4o and LLaMA 3 significantly improved accuracy (GPT-4o: 46% → 54%, P = .03; LLaMA 3: 17% → 28%, P = .01). Conclusion AcuKG is an open dataset that provides a structured and computational framework for acupuncture applications, bridging traditional practices with acupuncture research and cutting-edge LLM technologies.

Objective Raynaud phenomenon (RP) is often the initial clinical manifestation of systemic sclerosis (SSc), but some patients develop other manifestations first. To help elucidate the diversity of SSc presentation in its early stages, we describe the initial clinical manifestations and antinuclear antibody (ANA) profiles of patients in two early SSc cohorts. Methods All patient data in the Genetics vs Environment in Scleroderma Outcomes Study (GENISOS) and Collaborative National Quality and Efficacy Registry (CONQUER) cohorts were reviewed. Both studies enrolled patients within five years of the first non‐RP symptom. Results In GENISOS and CONQUER, respectively, 194 (44.2%) of 439 and 292 (31.1%) of 938 patients had a non‐RP initial symptom, most commonly puffy fingers/hands. Black patients had a non‐RP symptom before RP more commonly than patients in other race and ethnicity categories. Non‐RP first patients were more likely than RP first patients to have diffuse cutaneous involvement and joint contractures at enrollment and had a higher prevalence of RNA polymerase III antibody positivity. Conclusion In two large US cohorts, >30% of patients began to manifest SSc with puffy fingers/hands or other symptoms, without the “warning sign” of RP as their initial symptom. These patients presented with more severe skin and musculoskeletal disease on average, highlighting the importance of early recognition. The most common autoantibody associated with this presentation was RNA polymerase III. These results should be considered in efforts to recognize SSc in its earliest stages. Puffy fingers/hands, even in the absence of RP, should prompt consideration of early SSc and testing for ANA and SSc‐associated autoantibodies, including RNA polymerase III.

Abstract Glioblastoma is highly vascularized with increased perfusion and prognosis remains dismal, despite improved diagnosis and aggressive multimodal treatment. The current standard of care is maximal surgical resection when feasible, followed by adjuvant radiation with concurrent chemotherapy, temozolomide. However, treatment resistance and tumor recurrence pose a considerable challenge for stratifying patients for optimal treatment planning. Current radiological assessment of glioblastoma therapy response predominantly relies on morphological measures; that may not accurately capture early microscopic changes or therapeutic efficacy. We have developed and validated an advanced imaging method, Arterial Spin Labeling (ASL) MRI that enable noninvasive spatial assessment of the entire tumor. ASL-MRI uses water within the blood as an endogenous tracer and measures perfusion without the need for exogenous gadolinium-based contrast agent. The objective of this study was to investigate ASL measured quantitative perfusion after surgery to predict survival in glioblastoma patients undergoing the standard of care chemoradiation treatment. In this prospective, IRB-approved study, twenty-three adults with glioblastoma participated for longitudinal MRI. Progression-free survival (PFS) and overall survival (OS) were recorded for each patient. A baseline MRI scan was performed prior to chemoradiation treatment and after surgery utilizing a 3D ASL sequence. All structural images were co-registered to T1 post-contrast images. Tumor regions of interest (ROIs) such as enhancing tumor (ET), tumor core, whole tumor, and necrosis were manually segmented. Cerebral blood flow maps were calculated and mean perfusion values (MPV) within ET ROIs were extracted. Scatter plot of MPV and Kaplan-Meier graphs were generated using GraphPad Prism. Based on median survival of 420 days, patients were categorized into short (≤420 days) or long (>420 days) survival. A threshold ASL measured perfusion value of 30 mL/100g/min distinguished survival groups; hyper-perfusion correlated with shorter OS, while hypo-perfusion linked to longer OS. The mean±SD of PFS and OS for patients with shorter survival were 119±42 days and 230±117 days respectively, while for longer survival were 624±285 and 726±210 days respectively, and the values were statistically different (P<0.001for both). Kaplan-Meier curves showed significant OS and PFS differences (p<0.015, Hazard Ratio=3.056). Our data indicate that ASL measured baseline perfusion, after surgery and before chemoradiation treatment can benefit in predicting survival and treatment stratification in glioblastoma patients. ASL measured perfusion can be performed during radiation treatment and could provide valuable information that can be incorporated into emerging treatment strategies such as adaptive radiation. Citation Format: Durga Udayakumar, Limin Zhou, Marco Pinho, Yiming Wang, Ben Wagner, Michael Youssef, Joseph Maldjian, Ananth Madhuranthakam. Noninvasive magnetic resonance imaging measured quantitative perfusion as predictive and prognostic biomarker in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7163.

Abstract Objective The rapid evolution of the COVID-19 virus has led to the development of different vaccine shots, each designed to combat specific variants and enhance overall efficacy. While vaccines have been crucial in controlling the spread of the virus, they can also cause adverse events (AEs). Understanding these relationships is vital for vaccine safety monitoring and surveillance. Methods In our study, we collected data from the Vaccine Adverse Event Reporting System (VAERS) and social media platforms (Twitter and Reddit) to extract relationships between COVID-19 vaccine shots and adverse events. The dataset comprised 771 relation pairs, enabling a comprehensive analysis of adverse event patterns. We employed state-of-the-art GPT models, including GPT-3.5 and GPT-4, alongside traditional models such as Recurrent Neural Networks (RNNs) and BioBERT, to extract these relationships. Additionally, we used two sets of post-processing rules to further refine the extracted relations. Evaluation metrics including precision, recall, and F1-score were used to assess the performance of our models in extracting these relationships accurately. Results The most commonly reported AEs following the primary series of COVID-19 vaccines include arm soreness, fatigue, and headache, while the spectrum of AEs following boosters is more diverse. In relation extraction, fine-tuned GPT-3.5 with Sentence-based Relation Identification achieved the highest precision of 0.94 and a perfect recall of 1, resulting in an impressive F1 score of 0.97. Conclusion This study advances biomedical informatics by showing how large language models and deep learning models can extract relationships between vaccine shots and adverse events from VAERS and social media. These findings improve vaccine safety monitoring and clinical practice by enhancing our understanding of post-vaccination symptoms. The study sets a precedent for future research in natural language processing and biomedical informatics, with potential applications in pharmacovigilance and clinical decision-making.

Abstract Introduction COVID-19, a global health crisis, prompted rapid vaccine development. Bivalent vaccines by Pfizer-BioNTech and Moderna target both original and Omicron strains. Despite vaccination efforts, the persistence of adverse events underscores the importance of ongoing safety monitoring. Method We collected COVID-19 bivalent vaccine adverse event reports from the Vaccine Adverse Event Reporting System (VAERS) (September 15, 2022, to September 1, 2023). The Medical Dictionary for Regulatory Activities (MedDRA) categorized reported symptoms into corresponding concepts. Statistical analyses included zero-truncated Poisson regression for estimating symptom rates, logistic regression for identifying risk factors, and Spearman correlation for assessing non-parametric relationships between adverse events in different System Organ Classes (SOCs). Results Our analysis of COVID-19 bivalent vaccine adverse events identified significant correlations between SOCs. Notably, a positive correlation (ρ = 0.3) linked Infections (SOC 1) with Respiratory disorders (SOC 13), while a negative correlation (ρ=-0.3) connected Investigations (SOC 23) with Injury (SOC 24). Zero-truncated Poisson modeling estimated an average of 6.179 symptoms per individual, varying by age, gender, and vaccine. Gender and age significantly influence reported adverse events associated with COVID-19 bivalent vaccines. Females generally experienced higher odds for most SOCs compared to males, except for cardiac and product-related issues. Older adults showed a higher likelihood of symptoms across various SOCs, while children and adolescents had distinct susceptibility patterns. Additionally, Moderna and Pfizer vaccines were associated with different SOCs, with Moderna linked mainly to immune and skin disorders and Pfizer associated primarily with cardiac and metabolic issues. Conclusion In conclusion, our study aligned self-reported items with widely-accepted medical terminology and rigorously analyzed adverse events following COVID-19 bivalent vaccine using advanced statistical methods. The significant findings provide crucial insights, guiding evidence-based strategies and interventions for vaccine safety.

Abstract Objective Human Papillomavirus (HPV) vaccine is an effective measure to prevent and control the diseases caused by HPV. However, widespread misinformation and vaccine hesitancy remain significant barriers to its uptake. This study focuses on the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine. Methods We constructed the knowledge base (KB) for VaxBot-HPV, which consists of 451 documents from biomedical literature and web sources on the HPV vaccine. We extracted 202 question-answer pairs from the KB and 39 questions generated by GPT-4 for training and testing purposes. To comprehensively understand the capabilities and potential of GPT-based chatbots, 3 models were involved in this study: GPT-3.5, VaxBot-HPV, and GPT-4. The evaluation criteria included answer relevancy and faithfulness. Results VaxBot-HPV demonstrated superior performance in answer relevancy and faithfulness compared to baselines. For test questions in KB, it achieved an answer relevancy score of 0.85 and a faithfulness score of 0.97. Similarly, it attained scores of 0.85 for answer relevancy and 0.96 for faithfulness on GPT-generated questions. Discussion VaxBot-HPV demonstrates the effectiveness of fine-tuned large language models in healthcare, outperforming generic GPT models in accuracy and relevance. Fine-tuning mitigates hallucinations and misinformation, ensuring reliable information on HPV vaccination while allowing dynamic and tailored responses. The specific fine-tuning, which includes context in addition to question-answer pairs, enables VaxBot-HPV to provide explanations and reasoning behind its answers, enhancing transparency and user trust. Conclusions This study underscores the importance of leveraging large language models and fine-tuning techniques in the development of chatbots for healthcare applications, with implications for improving medical education and public health communication.

Background The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults. Objective To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults. Methods The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.e., age, sex, education), NPS (i.e., Neuropsychiatric Inventory Questionnaire; Beck Depression and Anxiety Inventories), at least one AD biomarker (i.e., plasma-, neuroimaging- and/or cerebrospinal fluid [CSF]-derived), and at least 2 repeated neuropsychological assessments. We trained and tested ML models using a stepwise feature addition approach to predict decline versus non-decline in global and domain-specific (i.e., memory, language, visuospatial, and attention/executive function) cognitive scores. Results ML models had better performance when NPS were included along with a) neuroimaging biomarkers for predicting decline in global cognition, as well as language and visuospatial skills; b) plasma-derived biomarkers for predicting decline in visuospatial skills; and c) CSF-derived biomarkers for predicting decline in attention/executive function, language, and memory. Conclusions NPS, added to ML models including demographic and AD biomarker data, improves prediction of downward trajectories in global and domain-specific cognitive scores among community-dwelling older adults, albeit effect sizes are small. These preliminary findings need to be confirmed by future cohort studies.