Citations

Abstract Background Corticobasal syndrome (CBS) is a neurodegenerative syndrome that often results from a 4-repeat tauopathy. Abnormalities on tau PET, structural MRI, and diffusion MRI (dMRI) have been observed in CBS, although no prior work has systematically compared the relative sensitivity of these modalities. In addition, the relationships between tau uptake, volume loss, and white matter degeneration remain incompletely understood. Objective To compare the sensitivity of tau PET, structural volume, and dMRI metrics to differentiate CBS from controls, and to characterize relationships between regional tau uptake, volume loss, and white matter tract abnormalities. Methods Thirty-two participants meeting criteria for possible or probable CBS and 35 healthy controls underwent standardized 3T dMRI, 18 F-flortaucipir tau PET, and detailed neurologic assessment. Diffusion data were processed using complementary diffusion tensor, free-water, and Neurite Orientation Dispersion and Density Imaging (NODDI) pipelines, with tract-level metrics extracted using the Johns Hopkins University-Eve (JHU EVE) White Matter atlas. Regional gray matter volumes and flortaucipir standardized uptake value ratios (SUVRs) were calculated. Global tau PET uptake, represented by the first principal component (PC1), was removed in a secondary tau analysis. Modality-level discrimination was assessed using area under the curve (AUC) analyses. Cross-modality relationships were evaluated using regional correlations and covariate-adjusted models comparing volume- and tau-related predictors of white matter abnormalities. Results Relative to controls, CBS showed widespread higher mean diffusivity (MD) and lower intracellular volume fraction (ICVF), especially in sensorimotor and projection white matter pathways. Structural volume reductions were most prominent in precentral cortex and subcortical regions including putamen, thalamus, and pallidum, whereas tau PET abnormalities were weaker and less spatially extensive. MD showed the strongest overall discrimination between CBS and controls, followed by ICVF, structural volume, PC1-removed tau PET, and original tau PET. In targeted sensorimotor analyses, higher DTI-MD was most strongly associated with lower gray matter volume. PC1-removed sensorimotor tau uptake was also associated with higher sensorimotor DTI-MD, whereas original tau uptake and broader cortical or subcortical tau measures were not significant predictors. Conclusions White matter microstructural disruption is a dominant imaging signature of CBS and is more closely linked to gray matter volume loss than to measurable uptake on tau PET. dMRI, particularly MD and ICVF, may provide greatest sensitivity for detecting disease-related changes in CBS.

ABSTRACT Distinguishing atypical parkinsonian disorders (APS) from Parkinson's disease (PD) remains challenging due to overlapping clinical features, yet accurate differentiation is critical for prognosis and treatment. Here, we employed multimodel diffusion MRI (dMRI) analysis to characterize microstructural alterations across corticobasal syndrome (CBS), progressive supranuclear palsy‐Richardson syndrome (PSP‐RS), and PD, with the aim of identifying which dMRI model and regional metrics show the strongest group‐level separation. We analyzed 25 CBS, 42 PSP‐RS, and 21 PD participants compared to 35 age and sex‐matched controls. Using a clinically feasible 3‐shell high angular resolution diffusion imaging (HARDI) protocol, we applied 11 metrics from five complementary dMRI models—diffusion tensor imaging (DTI), free‐water‐eliminated model of DTI (FWE), neurite orientation dispersion and density imaging (NODDI), tissue‐weighted NODDI, and fixel density (FD) in fixel‐based analysis (FBA)—to comprehensively assess regional white and gray matter integrity. Group differentiation was quantified using covariate‐adjusted Cohen's d effect sizes and spearman correlations were assessed between dMRI metrics and clinical scales. Distinct microstructural signatures were observed across disorders, and the sensitivity of the dMRI models differed. In group contrasts, DTI and NODDI‐derived metrics consistently captured the strongest effects in midbrain and peduncular pathways for PSP‐RS, whereas precentral and corticospinal alterations in CBS were most prominent using NODDI and FBA measures. Free‐water‐corrected metrics showed attenuated group differences. Across clinical‐diffusion analyses, NODDI metrics exhibited the most robust associations with disease severity, while DTI and FWE measures detected more limited, regionally constrained effects. Together, these findings highlight complementary yet distinct sensitivities of tensor, free‐water, multicompartment, and fixel‐based models to APS‐related neurodegeneration.

Abstract Context Incidental thyroid findings (ITFs) are increasingly detected on imaging performed for non-thyroid indications. Their prevalence, features, and consequences remain undefined. Objective To develop, validate, and deploy a natural language processing (NLP) pipeline to identify ITFs in radiology reports and assess their prevalence, features, and clinical outcomes. Design Retrospective cohort study. Setting Mayo Clinic sites (Rochester, Arizona, Florida, Mayo Clinic Health System). Participants Adults without prior thyroid disease undergoing thyroid-capturing imaging from July 1, 2017, to September 30, 2023. A transformer-based NLP pipeline identified ITFs and extracted nodule characteristics from image reports from multiple modalities and body regions. Outcomes ITF prevalence, downstream thyroid ultrasound, biopsy, thyroidectomy, and cancer diagnosis. Logistic regression identified demographic and imaging-related factors. Results Among 115 683 patients (mean age, 56.8 [SD 17.2]; 52.9% women), 9077 (7.8%) had an ITF (92.9% nodular). ITFs were more likely in women, older adults, higher BMI, and in imaging ordered by specialties different from Emergency Medicine. Compared with chest CT, ITFs were more likely via neck CT, PET, and nuclear medicine scans. Nodule characteristics were poorly documented, with size reported in 44% and other features in fewer than 15%. Compared with patients without ITFs, those with ITFs had higher odds of thyroid nodule diagnosis (OR 45, 95%CI 41.1-49.3), biopsy (OR 46.8, 95%CI 39.0-56.2) thyroidectomy (OR 55.8, 95%CI 31.3-99.3), and thyroid cancer diagnosis (OR 61.7, 95%CI 38.6-98.5). Most cancers were papillary (88.5%) and larger when detected after ITFs (2 cm SD 1.4) vs no ITF (1.3 cm SD 0.8). Conclusion ITFs were common and strongly associated with cascades leading to the detection of small, low-risk cancers, highlighting their role in overdiagnosis and the need for standardized reporting and more selective follow-up.

Abstract Distinguishing atypical parkinsonian disorders (APS) from Parkinson’s disease (PD) remains challenging due to overlapping clinical features, yet accurate differentiation is critical for prognosis and treatment. Here, we employed multi-model diffusion MRI (dMRI) analysis to characterize microstructural alterations across corticobasal syndrome (CBS), progressive supranuclear palsy-Richardson syndrome (PSP-RS) and PD, with the aim of identifying which dMRI model provides optimum differentiation. We analyzed 25 CBS, 42 PSP-RS, and 21 PD participants compared to 35 age and sex-matched controls. Using a clinically feasible 3-shell high angular resolution diffusion imaging (HARDI) protocol, we applied 11 metrics from five complementary dMRI models—diffusion tensor imaging (DTI), free-water-eliminated model of DTI (FWE), neurite orientation dispersion and density imaging (NODDI), tissue-weighted NODDI, and Fixel Density (FD) in fixel-based analysis (FBA) —to comprehensively assess regional white and gray matter integrity. Group differentiation was assessed using Cohen’s d effect sizes and spearman correlations were assessed between dMRI metrics and clinical scales. Distinct microstructural signatures were observed across disorders and the sensitivity of the dMRI models differed. In group contrasts, DTI and NODDI-derived metrics consistently captured the strongest effects in midbrain and peduncular pathways for PSP-RS, whereas precentral and corticospinal alterations in CBS were most prominent using NODDI and FBA measures. Free-water-corrected metrics showed attenuated group differences. Across clinical-diffusion analyses, NODDI metrics exhibited the most robust associations with disease severity, while DTI and FWE measures detected more limited, regionally constrained effects. Together, these findings highlight complementary yet distinct sensitivities of tensor, free-water, multi-compartment, and fixel-based models to APS-related neurodegeneration.

Abstract Purpose Overuse of thyroid ultrasound (TUS) has contributed to rising thyroid cancer diagnoses and is projected to increase US healthcare costs from $1.5 billion to $3.5 billion by 2030. This study evaluated the healthcare cost of inappropriately ordered TUS in a national multicenter academic system. Methods This is a secondary cost analysis of a retrospective cohort study across 4 Mayo Clinic sites (Rochester, MN; Jacksonville, FL; Scottsdale, AZ; and the Midwest Mayo Clinic Health System). Adult patients (≥18 years) undergoing their first TUS between January 1, 2017, and December 31, 2021, with at least 1 year of follow-up were included. TUS indications were classified as appropriate or inappropriate using a guideline-based natural language processing algorithm. The primary outcome was a comparison of adjusted 1-year all-cause healthcare costs. A secondary analysis calculated the direct procedural costs of the inappropriate TUS cascade. Results Among 6984 patients (mean age 56 [SD 16.4]; 76.2% female; 90.9% White), 546 (7.8%) underwent TUS for inappropriate indications. These patients were younger (mean age 53.0 vs 56.3 years, P < .0001) but otherwise demographically similar. Adjusted total healthcare costs over 90 days and 1 year were comparable: $4842 vs $5794 and $13 748 vs $14 257 for inappropriate vs appropriate TUS, respectively. The inappropriate TUS cascade, including an estimated 56 subsequent biopsies and 22 thyroidectomies, resulted in a minimum of $576 134 in direct procedural costs. Conclusion While adjusted total costs were similar, inappropriate TUS represents potentially avoidable spending and remains a viable target for cost-reduction strategies. Reducing low-value imaging remains a critical target for cost-saving interventions.

ImportanceThyroid cancer incidence has tripled in the past 3 decades, largely driven by increased detection of small, indolent papillary thyroid cancers. Overuse of thyroid ultrasonography (TUS) contributes to overdiagnosis, leading to unnecessary biopsies, procedures, and potential patient harm.ObjectiveTo evaluate the frequency of and factors associated with inappropriate TUS (iTUS) orders and assess related clinical outcomes.Design, Setting, and ParticipantsThis retrospective cohort study analyzed adult patients 18 years and older who underwent their first TUS from January 1, 2017, to December 30, 2021, at 4 Mayo Clinic sites. Patients with previous thyroid nodule, thyroid cancer, thyroid surgery, or TUS were excluded. Based on documented clinical indications, a validated natural language processing model classified TUS orders as appropriate or inappropriate. Data were analyzed from April 2024 to May 2025.ExposuresBaseline characteristics of patients, clinicians, and clinical encounters investigated for their association with an iTUS order.Main Outcomes and MeasuresThe primary outcome was the proportion of iTUS orders. Secondary outcomes included factors associated with iTUS (sociodemographic, clinical, order-specific, and clinician-related factors) and clinical outcomes following iTUS, including the detection of thyroid nodules, thyroid procedures, and thyroid cancer diagnoses.ResultsOf 11 442 included patients, 8422 (73.6%) were female, and the mean (SD) age was 57.3 (15.8) years with a mean (SD) body mass index (calculated as weight in kilograms divided by height in meters squared) of 29.7 (7.0). Among 11 442 TUS orders, 866 (7.6%) were classified as inappropriate. In multivariable analysis, younger age (age of 18 to 54 years vs older than 65 years; odds ratio [OR], 1.86; 95% CI, 1.56-2.22), hyperthyroidism (OR, 9.04; 95% CI, 6.75-12.11), ordering by nonendocrinology specialties (eg, oncology/hematology; OR, 3.43; 95% CI, 2.47-4.76), and orders not linked to an in-person appointment (eg, portal messages; OR, 2.42; 95% CI, 2.08-2.82) were strongly associated with increased odds of iTUS. Compared with appropriate TUS, iTUS led to lower rates of thyroid nodule detection (202 of 866 [23.3%] vs 6885 of 10 576 [65.1%]; OR, 0.16; 95% CI, 0.14-0.19), biopsies (89 [10.3%] vs 2647 [25.0%]; OR, 0.34; 95% CI, 0.27-0.43), partial thyroidectomies (14 [1.6%] vs 424 [4.0%]; OR, 0.39; 95% CI, 0.23-0.67), and confirmed thyroid cancer cases (16 [1.8%] vs 425 [4.0%]; OR, 0.45; 95% CI, 0.27-0.74).Conclusions and RelevanceIn this cohort study, approximately 1 in 13 TUS orders were inappropriate. iTUS was more common in younger patients, those with thyroid dysfunction, those not seen in person, and in those referred by nonendocrinology specialties. These findings highlight the need for targeted strategies to optimize TUS use and mitigate overdiagnosis and overtreatment.

Abstract Objectives The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs—a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care. Materials and Methods We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural Network for precise identification of patient primary concerns via multi-class classification. We examined 3 traditional machine learning models, 9 BERT-based language models, 6 fusion models, and 2 ensemble models. Results The outcomes of our experimentation underscore the superior performance achieved by BERT-based models in comparison to traditional machine learning models. Remarkably, our fusion model emerges as the top-performing solution, delivering a notably improved accuracy score of 77.67 ± 2.74% and an F1 score of 74.37 ± 3.70% in macro-average. Discussion This study highlights the feasibility and effectiveness of multi-class classification for patient primary concern detection and the proposed fusion framework for enhancing primary concern detection. Conclusions The use of multi-class classification enhanced by a fusion of multiple pretrained LMs not only improves the accuracy and efficiency of patient primary concern identification in PPMs but also aids in managing the rising volume of PPMs in healthcare, ensuring critical patient communications are addressed promptly and accurately.

Background: Prompt seeking of emergency medical services (EMS) assistance at stroke onset is critical to minimize poststroke disability. Objective: The aim was to study how racial differences in EMS decision-relevant factors and EMS use impact stroke care and disability outcomes. Design: A prospective observational study. Participants: A total of 1168 acute ischemic stroke patients discharged from April 2016 to October 2017 at a safety net hospital were included; 108 patients were surveyed before discharge. Measures: (1) Prehospital delay: EMS use, timely hospital arrival; (2) Stroke care: alteplase receipt and inpatient rehab; (3) Outcomes: Functional improvement at discharge (admission minus discharge scores on National Institutes of Health Stroke Scale), 90-day modified Rankin Scale; (4) EMS decision-relevant factors: Stroke symptom knowledge, source of knowledge, unfavorable past EMS/care experiences, and financial barriers to EMS use. Results: Despite more Black patients using EMS than Whites/Asians (56% vs. 48%, P=0.003), their timely hospital arrival was 30% less likely. Adjusted for stroke severity, receipt of alteplase, and inpatient rehab were similar, but Black patients fared worse on functional improvement at discharge (among severe strokes, 2.4 National Institutes of Health Stroke Scale points less improvement, P<0.01), and on functional normalcy at 90 days (modified Rankin Scale score 0–1 being 60% less likely across severity categories) (P<0.01). Fewer Black patients knew any stroke symptoms before the stroke (72% vs. 87%, P=0.03), and fewer learned about stroke from providers (P=0.01). Financial barriers and provider mistrust were similar. Conclusions: Black patients had less knowledge of stroke symptoms, more care-seeking delay, and poorer outcomes. Including stroke education as a standard of chronic disease care may mitigate stroke outcome disparities.

Abstract Photoacoustic tomography (PAT), a hybrid imaging modality that acoustically detects the optical absorption contrast, is a promising technology for imaging hemodynamic functions in deep tissues. Particularly, PAT is capable of measuring the blood oxygenation level using hemoglobin as the endogenous contrast. However, the most clinically compatible PAT configuration usually employs a linear ultrasound transducer array and often suffers from the poor image fidelity, mostly due to the limited detection view of the transducer array. PAT can be improved by employing highly-absorbing contrast agents such as droplets and nanoparticles, which, however, have low clinical translation potential due to safety concerns and regulatory hurdles. Moreover, unlike hemoglobin, these exogenous contrast agents cannot report the functional hemodynamic information. In this work, we have developed a new methodology that can improve PAT’s image fidelity without hampering its functional capability or clinical translation potential. By using clinically-approved microbubbles as virtual point sources that strongly scatter the local pressure waves generated by surrounding hemoglobin, we can overcome the limited-detection-view problem and achieve high-fidelity functional PAT in deep tissues, a technology referred to as virtual-point-source PAT (VPS-PAT). We have thoroughly investigated the working principle of VPS-PAT by numerical simulations and phantom validations, showing the acoustic origin of signal enhancement and the superiority over traditional PAT. We have also demonstrated proof-of-concept applications of functional VPT-PAT for in vivo small-animal studies with physiological challenges. We expect that VPS-PAT can find broad applications in biomedical research and accelerated translation to clinical impact.