Citations

Abstract Lichen Planopilaris (LPP) is a lymphocyte-mediated scarring alopecia characterized by progressive follicular destruction and fibrosis. In this clinical trial, patients with biopsy proven LPP were treated with deucravacitinib (an oral inhibitor of tyrosine kinase 2 (TYK2)) 6 mg BID for 24 weeks (NCT-06091956). Bulk and single-cell RNA sequencing was performed on paired pre- and post-treatment scalp biopsies from baseline and week 4. Patients (N=10) demonstrated improvements in PGA (88.9%, p=0.008), LPPAI (−2.3 points, SD 1.1, p=0.002) and Skindex-16 (−21.0 points, SD 22.1, p=0.014) scores at week 24. Bulk transcriptomic analysis of untreated LPP revealed upregulation of type I Interferon (IFN)-stimulated genes and pathways related to inflammation, immune activation, keratinization, and extracellular matrix remodelling, with downregulation of immune and inflammatory pathways following treatment. Single-cell RNA-seq of LPP was characterized by enrichment of CD8+GZMK+ T cells which showed downregulation of T-cell receptor signaling as well as antiviral pathways with treatment. Basal keratinocytes exhibited reduced cytokine and interferon signaling and decreased communication with NK cells following treatment. CCL19+ fibroblasts were prominent in untreated disease was attenuated after treatment, with downregulation of type I IFN signaling. Selective TYK2 inhibition with deucravacitinib effectively suppresses these inflammatory circuits in LPP and represents a promising therapeutic strategy.

Background Primary cutaneous diffuse large B-cell lymphoma, leg type (PCDLBCL-LT), is a rare subset of primary cutaneous lymphoma known for its aggressive behavior, poor prognosis, and limited treatment options, particularly in the relapsed or refractory setting. We report a case of a patient diagnosed with PCDLBCL-LT who achieved a clinically significant response and sustained remission for seven months with acalabrutinib, a covalent inhibitor of Bruton’s tyrosine kinase (BTK), before disease progression. Case presentation An 88-year-old woman presented with a rapidly enlarging ulcerated lesion on the left lower leg. Histopathological and immunohistochemical analyses confirmed the diagnosis of PCDLBCL-LT. She was treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemoimmunotherapy, followed by external beam radiation therapy (RT). These interventions resulted in only a partial response, and the disease progressed within several months. The patient was receiving direct oral anticoagulant (DOAC) therapy for left lower limb deep vein thrombosis, and she also had a history of hypertension. Considering the increased risk of major bleeding associated with DOAC use and the elevated risk of cardiovascular adverse events previously reported with ibrutinib, an alternative BTK inhibitor was considered. Accordingly, she was initiated on acalabrutinib at a dose of 100 mg orally twice daily. The patient exhibited a rapid and robust clinical response, with complete resolution of cutaneous lesions and sustained remission lasting seven months. Ultimately, the disease progressed, and given her advanced age and overall poor performance status, she was transitioned to hospice care. She subsequently died 14 months after initiating acalabrutinib. Conclusion This report documents the first known case of successful treatment of relapsed/refractory PCDLBCL-LT with acalabrutinib. Despite advanced age and poor performance status, the patient experienced rapid symptom resolution and durable remission without treatment-related adverse events. These findings suggest that BTK inhibitors may represent a promising therapeutic approach for PCDLBCL-LT, either as monotherapy or in combination with anti-CD20 antibodies. Further investigation into novel targeted therapies is urgently needed for this rare and aggressive lymphoma subtype.

Abstract Objective To investigate the ability of artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output and delta age, defined as AI‐estimated age minus chronological age, to predict adverse vascular outcomes in patients with migraine. Background Migraine, especially migraine with aura, is associated with an increased risk of cardiovascular events. Using normal sinus rhythm ECG, our institution has developed AI‐ECG models that predict the probability of AF and estimate patients' age, and from that, delta age (estimated age minus actual age) has been shown to reflect cardiovascular health and physiologic aging. Methods We conducted a retrospective cohort study that included adult patients with migraine who had at least one digital, standard 12‐lead ECG from 2000 to 2020 at any Mayo Clinic site. The first normal sinus rhythm ECG was defined as the index ECG, and those with adverse vascular events or AF prior to, or within 3 days after, index ECG were excluded. AI‐ECG AF prediction model output and delta age were obtained from the index ECG. The primary outcome was a composite endpoint of adverse vascular events that included acute myocardial infarction, acute ischemic stroke, venous thromboembolism, and death. New‐onset AF was also recorded as a secondary outcome. Results A total of 29,928 patients were included. The mean age was 44.3 ± 14.5 years, and the median follow‐up time was 54 months. During the study period, 4662 (15.6%) patients had adverse vascular events, and 1384 (4.6%) patients developed new‐onset AF. A higher AF prediction model output was associated with a higher risk of adverse vascular outcomes (adjusted hazard ratio [aHR]:1.15 [95% confidence interval: 1.12 to 1.18] for every 10% increase in prediction model output), as was a larger delta age (aHR:1.16 [1.12 to 1.21] for every 10‐year increase in delta age). Moreover, AI‐ECG AF prediction model outputs of ≥1% (aHR: 1.45 [1.36 to 1.54]) and delta age ≥−1 years (aHR: 1.09 [1.02 to 1.16]) were found to be the optimal cutoffs to dichotomize patients into high‐risk and low‐risk groups for adverse vascular events. Higher AI‐ECG AF prediction model output at baseline also predicted a higher likelihood of having new‐onset AF during the study period (aHR: 1.31 [1.26 to 1.37]), and the development of new‐onset AF was associated with a higher risk of adverse vascular events (aHR: 2.43 [2.17 to 2.73]). Conclusions For patients with migraine, a higher AI‐ECG AF prediction model output and delta age are predictive of adverse vascular outcomes. A higher AF prediction model output is also predictive of future AF development. Furthermore, we identified the cutoffs for both AI‐ECG models that can distinguish patients between high‐ and low‐risk groups. Our results showed that AI‐ECG models could inform clinical practice and identify patients with migraine at risk for future adverse vascular events.

Abstract Background Ventricular systolic dysfunction (LVSD) is the main predictor of mortality in Chagas disease (ChD). Although LVSD can be treated with affordable medications that enhance both symptoms and survival, its diagnosis necessitates advanced imaging tests, which are often unavailable in resource-limited settings. Recently, artificial intelligence (AI) models applied to an electrocardiogram (ECG) have shown promise in detecting LVSD and assessing disease progression, acting as digital biomarkers and offering the potential for screening tools. Purpose This study aims to evaluate the performance of an AI-ECG model in detecting LVSD and predicting mortality in patients with ChD. Specifically, we will fine-tune the AI-ECG model to improve LVSD identification and update the previously published mortality score (JAHA) by replacing NT-proBNP with AI-ECG to assess mortality risk. Methods This study was conducted with patients from the SaMi-Trop project, a prospective cohort of individuals seropositive for ChD from 21 municipalities in Minas Gerais, an endemic region of Brazil. The baseline assessment occurred in 2013–2014, with follow-ups in 2015–2016 (FU1) and 2022–2023 (FU2). The AI-ECG model was fine-tuned using a dataset with an 8% low ejection fraction prevalence. The model was trained with a binary cross-entropy loss function, a batch size of 128, and an Adam optimizer with a learning rate of 0.001. The area under the curve (AUC) was calculated for validation. The fine-tuned model, parsed into 2-second ECG windows, was applied to two waves of the SaMi-Trop cohort: Baseline (4,701 ECGs from 1,906 patients) and FU1 (2,324 ECGs from 1,059 patients). The association between AI-detected LVSD (AI-LVSD) and mortality was assessed using Cox proportional hazard models. Results The fine-tuned AI-LVSD model achieved an AUC of 0.88 (95% CI: 0.78–0.97) in detecting LVSD. When updating the JAHA mortality score, AI-LVSD replaced NT-proBNP with a stronger association with mortality risk, yielding hazard ratios (HR) of 4.5 (95% CI: 3,0–6.8) for two-year mortality and 4.3 (95% CI: 3.4–5.5) for overall mortality. Additionally, when incorporated into a simplified hazard model alongside age and NYHA class, AI-LVSD emerged as a strong biomarker for mortality risk, with hazard ratios of 5.4 (95% CI: 3.7–7.8) for two-year mortality and 4.9 (95% CI: 4.0–6.1) for overall mortality (Figure 1). Conclusion The AI-ECG model accurately identified LVSD for screening purposes and effectively replaced NT-proBNP in a simplified mortality risk model for Chagas disease. This tool can potentially enhance early detection and risk stratification, particularly in remote areas with limited access to advanced diagnostic resources.Figure 01.Cox proportional hazard model

Abstract Background Artificial intelligence (AI) models trained on 12-lead ECGs effectively detect left ventricular systolic dysfunction (LVSD; left ventricular ejection fraction [LVEF] <=40%). Continuous ECG monitoring via Holter recordings provides an opportunity for opportunistic screening for structural heart disease beyond rhythm disorders. We hypothesized that a lead-invariant version of the 12-lead AI model would enable a Holter monitor to screen for both arrhythmias and ventricular dysfunction. Methods We retrospectively analyzed continuous Holter ECGs from 17,665 patients who underwent a Holter and transthoracic echocardiogram (TTE) within 30 days of each other at Mayo Clinic. From each Holter, a random 20-minute of valid (non-flatline/lead disconnect) ECG segment was extracted and analyzed for LVSD detection using the adapted lead-invariant AI model. To evaluate stability, we examined model performance across different time points of the day, presenting results as area under the receiver operating characteristic curve (AUC) over time. Moreover, we illustrated the model’s robustness to noisy data by comparing its performance on raw ECG signals with that on bandpass-filtered inputs. Results Among 17,665 patients (mean age 59 years, 48.57% female), 4.96% had an LVEF <=40%. The AI model demonstrated strong predictive performance (20-minute segment AUC 0.90, mean prediction of 24-hour AUC 0.92). Analysis of results over time (Figure) revealed temporal patterns in predictive accuracy, with specific time periods showing greater stability. Despite modest variability, model performance remained consistently high throughout the day, confirming robustness across different physiological states. The predictions remained robust with noisy input. We did not observe performance improvement when the baseline wander and high frequency noise are removed by the bandpass filter. Conclusion Applying a 12-lead AI ECG model with a lead-invariant framework to a continuous Holter ECG enables effective screening for left ventricular dysfunction. This suggests that AI-based analysis of Holter-monitors can facilitate opportunistic screening of ventricular dysfunction and may enable assessment of an arrhythmia’s impact on LVEF, as well as the relationship between arrhythmia burden and LVEF.Figure 1.Mean prediction AUC of the day Figure 2.AUC for different time point

Abstract Cutaneous squamous cell carcinoma (cSCC) poses significant clinical challenges due to its rising incidence and potential for metastasis. Histopathologic risk stratification is further limited by substantial inter-observer variability. Unsupervised AI approaches based on content-based image retrieval offer scalable and interpretable decision support for diagnostic pathology. The objective of this study was to evaluate the use of image retrieval within histopathology atlases to stratify cSCC tumor differentiation from whole-slide images (WSIs), while comparing different patch selection and feature extraction strategies. This retrospective study included 552 archived WSIs comprising 385 well-differentiated, 102 moderately differentiated, and 66 poorly differentiated cases collected across Mayo Clinic sites in Arizona, Florida, and Minnesota. Image atlases were constructed using multiple patch aggregation strategies (Mosaic, Collage, and Montage) and deep learning encoders (KimiaNet, PathDino, and H-Optimus-0). A leave-one-WSI-out evaluation framework was used to assess differentiation classification performance using accuracy, specificity, sensitivity, and F1 score. Mosaic combined with KimiaNet achieved the highest Top-1 accuracy (74.9%) and specificity (92.6%), while Mosaic with H-Optimus-0 yielded the best Top-5 accuracy (79.0%) and macro-F1 score (62.6%). Collage combined with KimiaNet produced the highest Top-5 specificity (99.5%). The generalizability of the evaluated AI models varied across hospitals, reflecting differences in imaging protocols, staining practices, and patient populations. Overall, unsupervised image search and retrieval provides effective, annotation-free support for cSCC differentiation and has the potential to enhance dermatopathology workflows when appropriate combinations of patch selection and feature ex-traction methods are employed.

Background: Among the 1.5 million cases of cutaneous squamous cell carcinoma (cSCC) diagnosed annually in the United States, a subset of aggressive tumors recur or metastasize. Treatment options include total margin-controlled surgery, such as Mohs micrographic surgery or other forms of peripheral and deep en face margin assessment (MMS/PDEMA), and standard excision with postoperative vertical section margin assessment (SEVMA). NCCN Guidelines recommend MMS/PDEMA for the management of cSCC classified as very high risk based on NCCN risk stratification criteria. This study aims to validate the recommendation of MMS/PDEMA as the preferred surgical approach for NCCN very high-risk cSCCs, using data from a multicenter, multinational cohort. Methods: A multicenter cohort study was conducted across 12 sites, including 10 in the United States, 1 in Spain, and 1 in Brazil, comprising 2,752 primary NCCN very high-risk cSCCs treated with MMS/PDEMA or SEVMA. Propensity score–weighted analysis was used to balance baseline characteristics. Outcomes included local recurrence (LR), nodal metastasis (NM), distant metastasis (DM), and disease-specific death (DSD). Results: After balancing baseline characteristics, SEVMA was associated with a 2-fold higher rate of poor outcomes, including recurrence, metastasis, and DSD, compared with MMS/PDEMA. The 3-year cumulative incidence of poor outcomes was higher for SEVMA: LR (12.5% vs 5.9%), NM (11.6% vs 6.0%), DM (4.3% vs 1.9%), and DSD (6.2% vs 3.3%). Conclusions: This study supports the NCCN recommendation of MMS/PDEMA for NCCN very high-risk cSCC, showing reduced recurrence, metastasis, and cSCC-specific mortality compared with SEVMA.

Abstract Description Evidence consistent with immunoediting in human cancer has been identified through associative studies. To provide direct evidence that differences in the neoantigen profile are immune-mediated, we compared the neoantigen profiles of cutaneous squamous cell carcinoma (cSCC) from immunocompetent and immunosuppressed patients. Despite consistency in the overall mutational burden and signature, immunocompetent patients have a lower clonal mutational burden compared to immunosuppressed patients. Furthermore, clonal mutations in cSCC from immunocompetent patients have a lower proportion of neoantigens predicted to bind MHC class I, supporting the role of the immune system in shaping the neoantigen profile. In single-cell RNA sequencing of intratumoral CD8+ T cells from a separate cohort of cSCC from immunocompetent and immunosuppressed patients, we observe an increase in exhausted CD8+ T cells in tumors from immunocompetent patients, suggesting an explanation for the persistence of binding neoantigens subclonally. Finally, we compare clonal and subclonal neoantigens as an unbiased screen for characteristics associated with neoantigen immunogenicity. Neoantigens with a large increase in predicted MHC class I binding relative to the normal peptide are enriched in subclonal neoantigens in immunocompetent patients. Overall, this work supports the immune system’s role in sculpting the neoantigen profile of cSCC. Funding Sources Supported by American Cancer Society - Simone Charitable Foundation - Discovery Boost Grant, AZ-DBG-23-1151311-01-IBCD; NIH F30CA281056; R35GM149235 Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)

Introduction: Diastolic dysfunction is a hallmark of heart failure with preserved ejection fraction, yet echocardiographic grading is challenging in patients with hypertrophic or amyloid cardiomyopathies due to variable structural and functional remodeling. Consequently, there is a need for additional diagnostic tools to identify patients at increased risk of diastolic dysfunction and enhanced filling pressure. Research Question: What is the accuracy of an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to predict diastolic dysfunction in hypertrophic and amyloid cardiomyopathy? Methods: We included patients with diverse phenotypes of hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis and validated Mayo Clinic's AI-enabled ECG model to grade diastolic function (normal: grade 0/1; abnormal: grade 2/3). Echocardiographic analysis of systolic (LVEF) and diastolic function was performed within 14 days of ECG recording. Diastolic function was graded based on E/e’ (>14), left atrial volume index (>34 mL/m2), pulmonary venous atrial reversal flow duration (≥30 ms), and peak tricuspid regurgitation velocity (>2.8 m/s). Filling pressure was considered increased if ≥3 parameters were abnormal, inconclusive if 2 were abnomal, and normal if ≥3 were normal. Echocardiograms with severe mitral regurgitation were excluded. Results: We identified and matched 840 ECGs from non-obstructive HCM patients (n=125, age 58±19y, 70% male), 145 ECGs from obstructive HCM patients (n=29, age 56±18y, 66% male), 225 ECGs from apical HCM patients (n=33, age 66±14y, 61% male), and 382 ECGs from cardiac amyloidosis patients (n=49, age 70±13y, 57% male). The AI-enabled ECG model predicted diastolic dysfunction with an accuracy of 65% (non-obstructive HCM), 67% (obstructive HCM), 65% (apical HCM), and 75% (cardiac amyloidosis), with sensitivities of 94% and 97% for obstructive HCM and cardiac amyloidosis, respectively, and high positive and negative predictive values for cardiac amyloidosis. Systolic dysfunction (LVEF≤40%) was more common in cardiac amyloidosis (29%) than in all HCM patients (5%). Conclusion: The AI-enabled ECG model demonstrates moderate accuracy but high sensitivity for detecting diastolic dysfunction and increased filling pressure, especially in obstructive HCM and cardiac amyloidosis. The AI-enabled ECG is a useful, widely scalable, and low cost tool to identify patients at increased risk for diastolic dysfunction.