BACKGROUND:Right ventricular (RV) dysfunction in pulmonary hypertension due to heart failure with preserved ejection fraction (PH-HFpEF) leads to adverse outcomes, yet the mechanisms underlying RV failure remain incompletely defined. We aimed to develop a multimodal framework integrating vascular mechanics and myocardial transcriptomics for a mechanistic understanding of RV dysfunction in PH-HFpEF. METHODS:In a 2-step study, a predominantly retrospective PH-HFpEF cohort (n=48) underwent comprehensive assessment with clinical evaluation, echocardiography, cardiac magnetic resonance imaging (MRI), and invasive cardiopulmonary exercise testing. Based on cardiac MRI-derived RV ejection fraction <45%, the PH-HFpEF cohort was stratified into a normal RV function group (n=29) and an RV dysfunction group (n=19). A prospective subset underwent pulmonary vascular mechanics (impedance and wave intensity analysis, n=17), 4-dimensional flow cardiac MRI (n=15), and endomyocardial biopsy with long-read RNA sequencing (n=10). RESULTS:PH-HFpEF participants with RV dysfunction had worse 1-year outcomes (mortality or first heart failure hospitalization; hazard ratio, 8.2 [95% CI, 2.5-25.4]) and exhibited multisystem limitations (abnormal cardiac reserve, pulmonary vascular, and ventilatory function). Compared with the normal RV subgroup, the RV dysfunction subgroup had impaired left ventricular longitudinal strain on cardiac MRI. Pulmonary vascular mechanics demonstrated increased proximal pulmonary arterial stiffness (characteristic impedance), increased RV energy expenditure, and abnormal distal vascular reflections with exercise, indicating segmental pulmonary vascular remodeling. Four-dimensional flow MRI revealed disturbed flow patterns and trends toward increased viscous energy loss across the left heart and pulmonary circulation. Global gene differences were minimal, likely reflecting the limited statistical power for detecting individual differentially expressed genes in this modest cohort; however, pathway analysis revealed upregulation of RNA metabolism and downregulation of mitochondrial pathways in the RV dysfunction subgroup. Long-read sequencing further identified selective isoform expression in key cardiac genes, highlighting differential regulation in PH-HFpEF with RV dysfunction. CONCLUSIONS:This integrative methodological framework of vessel-specific wave mechanics and myocardial transcriptomics advances the mechanistic understanding of left heart-pulmonary vascular remodeling in PH-HFpEF.

Detecting human papillomavirus (HPV) status is crucial for treating Head and Neck Squamous Cell Carcinomas (HNSCCs). While p16 immunohistochemistry is the current standard for HPV detection, its moderate sensitivity and complex implementation limit its global utility. The ability to diagnose HPV status in HNSCC has become increasingly critical worldwide, as rising HPV-positive HNSCC rates observed in high-income countries may signal a global trend, and HPV status remains essential for treatment selection. Although hematoxylin and eosin (H&E) stained slides are clinically ubiquitous, artificial intelligence (AI) methods applied to these images have not matched molecular assays’ performance nor provided needed clinical interpretability. Recent advances in vision transformer-based foundation models for computational pathology offer a promising approach to address this unmet need. We analyzed H&E images from 981 HNSCC patients across four datasets: TCGA (n=HPV+ 33/total 401), CPTAC (n=1/109), UCH (n=159/364), and PENN (n=106/106). Fifty percent of the patients were used for validation. Using UNI, a foundation self-supervised learning (SSL) model, we extracted feature vectors from 10x effective magnification tile images across each whole slide. We identified an HPV feature axis using recursive support vector machine and principal component analysis to isolate SSL features that differentiated HPV tumors, then interpreted the relevant histologic features using HistoXGAN to generate synthetic histology images. This approach enabled the isolation of histologic features specific to HPV+ tumors, whereas real histology images contain multiple sources of variation. An expert pathologist validated the biological relevance of the identified HPV-axis features. We developed a predictive model for HPV status by varying the percentages of tiles within each slide required to exceed a binary threshold on the HPV-axis. This model was robust across threshold choices. Our method identified an HPV-axis that robust HPV detection performance (sensitivity 0.83, specificity 0.88) across all datasets (balanced accuracy - TCGA 0.77, CPTAC 0.98, UCH 0.78, PENN 1.00). Using synthetic histology images and pathologist validation, we identified key, morphological features aligning with established HPV-associated histology, including nuclei size, color, and cell borders. Together with our Grundium slide scanning pipeline the time-to-prediction for a slide is less than 3 minutes. Foundation models and synthetic digital pathology enabled HPV detection from histology with accuracy comparable to current diagnostic standards, while providing pathologist-interpretable predictions. This accessible, rapid, and explainable method holds promise for expanding testing of HPV and potentially other molecular features in resource-limited settings. Hanna M. Hieromnimon, Anna Trzcinska, Frank Wen, Frederick M. Howard, James M. Dolezal, Emma Dyer, Sara Kochanny, Jefree Schulte, Cindy Wang, Heather Chen, Jeffrey Chin, Elizabeth Blair, Nishant Agrawal, Ari Rosenberg, Everett Vokes, 1 Rohan Katipally, Aditya Juloori, Evgeny Izumchenko, Mark W. Lingen, Nicole Cipriani, Jalal B. Jalaly, Devraj Basu, Samantha J. Riesenfeld, Alexander T. Pearson. Interpretable HPV detection in head and neck cancer using foundation models and synthetic digital pathology [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 2471.

BACKGROUND:Cardiac sarcoidosis (CS) is a rare but clinically significant condition in heart transplant recipients. CASE SUMMARY:A 64-year-old woman with nonischemic cardiomyopathy and heart transplant presented with exertional dyspnea and new reduction in left ventricular ejection fraction. Endomyocardial biopsy, donor-specific antibodies, and imaging were concerning for antibody-mediated rejection versus CS. Empiric treatment for antibody-mediated rejection was initiated, but her left ventricular ejection fraction remained unchanged. Native heart explant was reviewed and was consistent with CS. Methotrexate was started, with clinical and radiographic improvement. DISCUSSION:Owing to its patchy nature, CS can be difficult to diagnose on biopsy, but cardiac imaging can assist. Heart transplant recipients with known CS have similar outcomes to transplant recipients without CS if they take low-dose steroids indefinitely. TAKE-HOME MESSAGE:The explanted heart should be extensively examined for CS in transplant recipients; if found, clinicians should prescribe CS-effective therapy indefinitely to prevent recurrence.

Objectives Human papillomavirus (HPV) influences the pathobiology of Head and Neck Squamous Cell Carcinomas (HSNCCs). While deep learning shows promise in detecting HPV from hematoxylin and eosin (H&E) stained slides, the histologic features utilized remain unclear. This study leverages artificial intelligence (AI) foundation models to characterize histopathologic features associated with HPV presence and objectively describe patterns of variability in the HPV-positive space. Materials and Methods H&E images from 981 HNSCC patients across public and institutional datasets were analyzed. We used UNI, a foundation model based on self-supervised learning (SSL), to map the landscape of HNSCC histology and identify the axes of SSL features that best separate HPV-positive and HPV-negative tumors. To interpret the histologic features that vary across different regions of this landscape, we used HistoXGAN, a pretrained generative adversarial network (GAN), to generate synthetic histology images from SSL features, which a pathologist rigorously assessed. Results Analyzing AI-generated synthetic images found distinctive features of HPV-positive histology, such as smaller, paler, more monomorphic nuclei; purpler, amphophilic cytoplasm; and indistinct cell borders with rounded tumor contours. The SSL feature axes we identified enabled accurate prediction of HPV status from histology, achieving validation sensitivity and specificity of 0.81 and 0.92, respectively. Our analysis subdivided image tiles from HPV-positive histology into three overlapping subtypes: border, inflamed, and stroma. Conclusion Foundation-model-derived synthetic pathology images effectively capture HPV-related histology. Our analysis identifies distinct subtypes within HPV-positive HNSCCs and enables accurate, explainable detection of HPV presence directly from histology, offering a valuable approach for low-resource clinical settings.

In summary, pulmonary vascular changes are commonly observed by the pulmonary pathologist, and the majority of these changes are nonspecific and due to secondary processes. Primary (idiopathic) pulmonary vascular changes often include more severe or extreme changes including plexiform lesions and pulmonary venous occlusion. Vasculitis and capillaritis can be seen in surgical pathology specimens and are often associated with ANCA-vasculitis or collagen vascular disease. Specific vascular changes can also be observed in the neonate and posttransplant setting.

Background:Pulmonary hypertension due to heart failure with preserved ejection fraction (PH-HFpEF) is a highly heterogeneous disease associated with right ventricular (RV) failure and adverse outcomes. Current diagnostic tools inadequately characterize pulmonary vascular disease and RV dysfunction, limiting treatment precision. We hypothesized that deep phenotyping-including invasive hemodynamics, advanced imaging, and myocardial transcriptomics-would identify high-risk PH-HFpEF phenotypes with distinct clinical, physiologic, and molecular characteristics. Methods:42 PH-HFpEF participants (and 25 pre-capillary PH participants, as a comparison group) underwent clinical evaluation, echocardiography, cardiac MRI (cMRI), and invasive cardiopulmonary exercise testing (iCPET). K-means clustering considering clinical, iCPET, and cMRI data stratified participants into distinct clusters (phenogroups). A subset underwent further characterization by invasive pulmonary vascular mechanics (n=17 PH-HFpEF, n=5 pre-capillary PH) and 4D flow cMRI (n=10 PH-HFpEF, n=5 pre-capillary PH). Endomyocardial biopsies from 10 PH-HFpEF participants were analyzed using long-read RNA-sequencing for differential gene and transcript expression. Results:Clustering revealed two PH-HFpEF phenogroups with significantly different outcomes at one-year follow-up (HR=11.96, CI: 2.66-53.86). High-risk participants had greater left ventricular mass, reduced RV ejection fraction, worse exercise-induced PH, impaired gas exchange (↓peak oxygen consumption, ↑slope of minute ventilation/carbon dioxide production), and lower myocardial strain. Pulmonary vascular mechanics showed higher proximal pulmonary artery stiffness (↑characteristic impedance), increased RV energy expenditure (↑compression waves), and abnormal distal vascular reflections (↓diastolic reflection index) in the high-risk group. 4D flow MRI revealed disturbed flow in pulmonary vasculature (both arteries and veins) in high-risk participants. Transcriptomic analysis implicated differences in post-transcriptional RNA processing and translational control, as well as mitochondrial function, in phenotypic divergence between these HFpEF subgroups. In particular, differential transcript usage for the TTN gene, encoding the giant sarcomeric protein titin, may be particularly relevant to elevated myocardial stiffness, which worsens vascular remodeling and precipitates RV dysfunction and failure. Conclusions:In PH-HFpEF, unsupervised clustering using deep physiologic and imaging data identified a high-risk group with impaired RV function and unique transcriptomic profiles. Our findings suggest that proximal and distal pulmonary vascular remodeling, as well as differences in titin isoform expression, may underlie RV failure in this phenogroup. This comprehensive approach provides a framework for mechanistically driven precision medicine strategies in PH-HFpEF. Clinical perspectives:What is new?: 1.Unsupervised clustering integrating exercise hemodynamics, cardiac MRI, and clinical features revealed a distinct PH-HFpEF subgroup at significantly higher risk for adverse outcomes (HR = 11.96), independent of traditional IpcPH/CpcPH classification.Invasive wave mechanics (impedance and wave separation analyses) and non-invasive 4D flow cardiopulmonary MRI reveal unique patterns of proximal stiffness, RV energy inefficiency, and abnormal distal reflections in high-risk PH-HFpEF, providing insights into segmental vascular dysfunction.Long-read RNA sequencing of endomyocardial biopsies from PH-HFpEF patients revealed distinct transcriptomic signatures between high- and low-risk PH-HFpEF phenogroups.Long-read RNA-seq identified distinct transcript usage of gene TTN, encoding a giant sarcomeric protein (titin) that is a key determinant for myocardial stiffness, suggesting a potential underlying mechanism for RV dysfunction in PH-HFpEF.What are clinical implications?: 2.These findings offer a framework for more refined patient classification beyond traditional PVR-based methods, which may improve identification of high-risk individuals who require closer monitoring or targeted therapy.The integrative approach highlights titin isoform imbalance and vessel-specific stiffness as potential therapeutic targets, supporting the development of biology-informed, phenotype-specific interventions for PH-HFpEF with RV dysfunction.

In a susceptible individual, persistent, low-level injury to the airway epithelium initiates an exaggerated wound repair response, ultimately leading to idiopathic pulmonary fibrosis (IPF). The mechanisms driving this fibroproliferative response are not fully understood. Here, we review recent spatially resolved transcriptomics and proteomics studies that provide insight into two distinct matricellular microenvironments important in this pathological fibroproliferation. First, in response to alveolar epithelial injury, alveolar differentiation intermediate (ADI) basal cells arising from Secretoglobin (Scgb1a1) progenitors re-populate the injured alveolus remodeling the extracellular matrix (ECM). ADI cells exhibit an interconnected cellular stress response involving the unfolded protein response (UPR), epithelial–mesenchymal transition (EMT) and senescence pathways. These pathways reprogram cellular metabolism to support fibrillogenic ECM remodeling. In turn, the remodeled ECM tonically stimulates EMT in the ADI population, perpetuating the transitional cell state. Second, fibroblastic foci (FF) are a distinct microenvironment composed of activated aberrant “basaloid” cells supporting transition of adjacent mesenchyme into hyaluronan synthase (HAShi)-expressing fibroblasts and myofibroblasts. Once formed, FF are the major matrix-producing factories that invade and disrupt the alveolar airspace, forming a mature scar. In both microenvironments, the composition and characteristics of the ECM drive persistence of atypical epithelium sustaining matrix production. New approaches to monitor cellular trans-differentiation and matrix characteristics using positron emission tomography (PET)–magnetic resonance imaging (MRI) and optical imaging are described, which hold the potential to monitor the effects of therapeutic interventions to modify the ECM. Greater understanding of the bidirectional interrelationships between matrix and cellular phenotypes will identify new therapeutics and diagnostics to affect the outcomes of this lethal disease.

Deployment and access to state-of-the-art diagnostic technologies remains a fundamental challenge in providing equitable global cancer care to low-resource settings. The expansion of digital pathology in recent years and its interface with computational biomarkers provides an opportunity to democratize access to personalized medicine. Here we describe a low-cost platform for digital side capture and computational analysis composed of open-source components. The platform provides low-cost ($200) digital image capture from glass slides and is capable of real-time computational image analysis using an open-source deep learning (DL) algorithm and Raspberry Pi ($35) computer. We validate the performance of deep learning models’ performance using images captured from the open-source workstation and show similar model performance when compared against significantly more expensive standard institutional hardware.

Mesothelioma is a rare disease with an historically poor prognosis. Over the past decade, a grading system has been developed that is a powerful prognostic tool in epithelioid mesothelioma. Grading of epithelioid mesothelioma is now required or strongly recommended by expert consensus, the College of American Pathologists, the World Health Organization, and the International Mesothelioma Interest Group. The original nuclear grading system for epithelioid mesothelioma, developed in the United States, split epithelioid mesotheliomas into three prognostic groups with marked differences in survival. Now, this three-tiered nuclear grading system has been combined with the presence or absence of necrosis to form the currently recommended two-tiered grading system of low- and high-grade epithelioid mesothelioma. This review will focus on the development of this grading system in mesothelioma, the grading system's shortcomings, and the application of the grading system to cytology specimens and other extra-pleural sites. Lastly, this review will briefly discuss alternative grading systems and future considerations.

Introduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.

Context.— Mesothelioma is an uncommon tumor that can be difficult to diagnose. Objective.— To provide updated, practical guidelines for the pathologic diagnosis of mesothelioma. Data Sources.— Pathologists involved in the International Mesothelioma Interest Group and others with expertise in mesothelioma contributed to this update. Reference material includes peer-reviewed publications and textbooks. Conclusions.— There was consensus opinion regarding guidelines for (1) histomorphologic diagnosis of mesothelial tumors, including distinction of epithelioid, biphasic, and sarcomatoid mesothelioma; recognition of morphologic variants and patterns; and recognition of common morphologic pitfalls; (2) molecular pathogenesis of mesothelioma; (3) application of immunohistochemical markers to establish mesothelial lineage and distinguish mesothelioma from common morphologic differentials; (4) application of ancillary studies to distinguish benign from malignant mesothelial proliferations, including BAP1 and MTAP immunostains; novel immunomarkers such as Merlin and p53; fluorescence in situ hybridization (FISH) for homozygous deletion of CDKN2A; and novel molecular assays; (5) practical recommendations for routine reporting of mesothelioma, including grading epithelioid mesothelioma and other prognostic parameters; (6) diagnosis of mesothelioma in situ; (7) cytologic diagnosis of mesothelioma, including use of immunostains and molecular assays; and (8) features of nonmalignant peritoneal mesothelial lesions.

Technological development of microwave treatment and detection techniques for lung cancer requires accurate and comprehensive knowledge of the microwave dielectric properties of human lung tissue. We characterize the dielectric properties of room temperature human lung tissue from 0.5 to 10 GHz for three lung tissue groups: normal, fibroelastotic, and malignant. We fit a two-pole Debye model to the measured frequency-dependent complex permittivity and calculate the median Debye parameters for the three groups. We find that malignant lung tissue is approximately 10% higher in relative permittivity and conductivity compared to normal lung tissue; this trend matches previously reported normal versus malignant data for other biological tissues. There is little contrast between benign lung tissue with fibroelastosis and malignant lung tissue. We extrapolate our data from room temperature to 37 °C using a temperature-dependence model for animal lung tissue and use the Maxwell-Garnett dielectric mixing model to predict the dielectric properties of inflation-dynamic human lung tissue; both approximations correspond with previously reported dielectric data of bovine and porcine lung tissue.

Objectives Mesothelioma is a lethal disease that arises from the serosal lining of organ cavities. Several recurrent alterations have been observed in pleural and peritoneal -mesotheliomas, including in BAP1, NF2, and CDKN2A. Although specific histopathologic parameters have been correlated with prognosis, it is not as well known whether genetic alterations correlate with histologic findings. Methods We reviewed 131 mesotheliomas that had undergone next-generation sequencing (NGS) at our institutions after pathologic diagnosis. There were 109 epithelioid mesotheliomas, 18 biphasic mesotheliomas, and 4 sarcomatoid mesotheliomas. All our biphasic and sarcomatoid cases arose in the pleura. Of the epithelioid mesotheliomas, 73 were from the pleura and 36 were from the peritoneum. On average, patients were 66 years of age (range, 26-90 years) and predominantly male (92 men, 39 women). Results The most common alterations identified were in BAP1, CDKN2A, NF2, and TP53. Twelve mesotheliomas did not show a pathogenic alteration on NGS. For epithelioid mesotheliomas in the pleura, the presence of an alteration in BAP1 correlated with low nuclear grade (P = .04), but no correlation was found in the peritoneum (P = .62). Similarly, there was no correlation between the amount of solid architecture in epithelioid mesotheliomas and any alterations in the pleura (P = .55) or peritoneum (P = .13). For biphasic mesotheliomas, cases with either no alteration detected or with an alteration in BAP1 were more likely to be epithelioid predominant (>50% of the tumor, P = .0001), and biphasic mesotheliomas with other alterations detected and no alteration in BAP1 were more likely to be sarcomatoid predominant (>50% of the tumor, P = .0001). Conclusions This study demonstrates a significant association between morphologic features associated with a better prognosis and an alteration in BAP1.