Acute Ischemic Stroke Superimposed on Acute Pulmonary Embolism in the Presence of Patent Foramen Ovale: A Case Report.

A patent foramen ovale (PFO) carries a high risk of paradoxical embolism. This risk is higher in certain conditions, including acute pulmonary embolism (APE). Although most patients with a PFO are asymptomatic, various clinical manifestations may be associated with PFO. Concomitant APE and acute ischemic stroke (AIS) due to paradoxical embolism from a PFO are rare. We report a case of a 61-year-old man who presented with simultaneous PE and AIS in the presence of PFO, was treated successfully with anticoagulation, and was discharged from the hospital neurologically intact.

Histopathological evaluation of abdominal aortic aneurysms with deep learning.

Computational analysis of histopathological specimens holds promise in identifying biomarkers, elucidating disease mechanisms, and streamlining clinical diagnosis. However, the application of deep learning techniques in vascular pathology remains underexplored. Here, we present a comprehensive evaluation of deep learning-based approaches to analyze digital whole-slide images of abdominal aortic aneurysm samples from 369 patients from three European centers. Deep learning demonstrated robust performance in predicting inflammatory characteristics, particularly in the adventitia, as well as fibrosis grade and remaining elastic fibers in the tunica media. Overall, this study represents the first comprehensive evaluation of computational pathology in vascular disease and has the potential to contribute to improved understanding of abdominal aortic aneurysm pathophysiology and personalization of treatment strategies, particularly when integrated with radiological phenotypes and clinical outcomes.

When Hearts and Minds Collide: A Case Report of Left Ventricle Noncompaction Syndrome as a Precursor to Acute Ischemic Stroke in a 15-Year-Old Along With a Literature Review.

Pediatric stroke, though uncommon, is often underdiagnosed due to subtle symptoms and delayed recognition. Cardiac diseases, accounting for up to 33% of pediatric ischemic strokes, play a significant role. This case report explores the rare occurrence of ischemic stroke in a 15-year-old boy with left ventricular non-compaction syndrome (LVNC). It underscores the complexity of managing pediatric ischemic stroke, particularly in the context of LVNC, emphasizing the challenges in timely diagnosis and management.

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.

Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression.

Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1-stained slides from the non-small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1-2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96-2.2), P = 0.082] and CPS [HR: 1.2 (0.79-1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity. SIGNIFICANCE: The weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment.

Direct image to subtype prediction for brain tumors using deep learning.

Background: Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides. Methods: We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising N = 2845 patients. Results: We found that the key molecular alterations for subtyping, IDH and ATRX, as well as 1p19q codeletion, were predictable from histology with an area under the receiver operating characteristic curve (AUROC) of 0.95, 0.90, and 0.80 in the training cohort, respectively. These findings were upheld in external validation cohorts with AUROCs of 0.90, 0.79, and 0.87 for prediction of IDH, ATRX, and 1p19q codeletion, respectively. Conclusions: In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available.

Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study.

Background: Homologous Recombination Deficiency (HRD) is a pan-cancer predictive biomarker that identifies patients who benefit from therapy with PARP inhibitors (PARPi). However, testing for HRD is highly complex. Here, we investigated whether Deep Learning can predict HRD status solely based on routine Hematoxylin & Eosin (H&E) histology images in ten cancer types. Methods: We developed a fully automated deep learning pipeline with attention-weighted multiple instance learning (attMIL) to predict HRD status from histology images. A combined genomic scar HRD score, which integrated loss of heterozygosity (LOH), telomeric allelic imbalance (TAI) and large-scale state transitions (LST) was calculated from whole genome sequencing data for n=4,565 patients from two independent cohorts. The primary statistical endpoint was the Area Under the Receiver Operating Characteristic curve (AUROC) for the prediction of genomic scar HRD with a clinically used cutoff value. Results: We found that HRD status is predictable in tumors of the endometrium, pancreas and lung, reaching cross-validated AUROCs of 0.79, 0.58 and 0.66. Predictions generalized well to an external cohort with AUROCs of 0.93, 0.81 and 0.73 respectively. Additionally, an HRD classifier trained on breast cancer yielded an AUROC of 0.78 in internal validation and was able to predict HRD in endometrial, prostate and pancreatic cancer with AUROCs of 0.87, 0.84 and 0.67 indicating a shared HRD-like phenotype is across tumor entities. Conclusion: In this study, we show that HRD is directly predictable from H&E slides using attMIL within and across ten different tumor types.

Pulmonary Artery Intimal Sarcoma Mimicking Right Ventricular Outflow Tract Thrombus.

A 72-year-old man presented with breathlessness and a systolic murmur. Extensive diagnostic work-up revealed a rare pulmonary artery intimal sarcoma mimicking a right ventricular outflow tract thrombus and impacting a cardiac pacemaker lead. Surgical resection, pathology confirmation, and management strategies are discussed, highlighting the challenges of treating this rare malignancy.

A Rare Case of a Chemical Burn: Dithranol with Salicylic Acid.

A 16-year-old female with psoriasis presented to our Plastic Surgery Department with a significant chemical burn to the neck, upper torso and left cheek (TBSA 6%). She applied a concoction of cream prescribed by her dermatologist in her native country, Poland when she returned to the United Kingdom. A few hours after application she developed a burn with pH of 5. A review of the cream revealed a mixture of 19% dithranol and 5% salicylic acid. This combination is recognized for managing psoriasis, however the strength of dithranol in the combination given is of a high concentration (normally <3%). This alone can cause a burn to the skin if left for a prolonged period of time. Salicylic acid is an enhancer which augments the stability of dithranol and increases its penetration and efficacy. The concentration of 5% is also on the higher end. Our patient was admitted for pain relief and further irrigation till normalization of the pH which was achieved after 3 days. A worrying aspect in our patients' case is that she was given the cream to commence at home. High concentration preparation is normally commenced in a controlled setting under medical supervision.

Athlete ECG T-wave abnormality interpretation patterns by non-experts.

Background: The presence of T-wave abnormalities (TWA) on an athlete's electrocardiogram (ECG) presents as a diagnostic challenge for physicians. Types of TWA patterns classified as abnormal by inexperienced readers have not been systematically analyzed. Methods: ECGs from the 2011-2015 National Football League Scouting Combine (initially interpreted by general cardiologists) were retrospectively reviewed by expert sports cardiologists with strict application of the 2017 International Criteria. Patterns of TWA that were altered from the original interpretation were analyzed. Results: The study included 1643 athletes (mean age 22 years). There was a 67 % reduction in the number of athletes with any TWA (p < 0.001) with 111 ECGs changed to normal. Inferior TWA was the most common interpreted initial ECG abnormality altered followed by anterior and lateral. Discussion: This analysis revealed an initial high rate of TWA by non-expert readers. Tailored education programs to physicians who interpret athlete ECGs should highlight these specific T-wave patterns. We see this as an opportunity to make more clinicians aware of ECG interpretation guidelines as sports trained cardiologists are mostly self-taught.

Incremental Value of Global Longitudinal Strain for Predicting Survival in Patients With Advanced AL Amyloidosis.

BACKGROUND: Advanced light-chain (AL) amyloidosis is associated with poor prognosis, with a 5-year survival rate of <25%. Prognostication is based on the revised Mayo (rMayo) staging according to serum cardiac biomarkers. OBJECTIVES: This study sought to determine whether global longitudinal strain (GLS) can provide incremental prognostic value in patients with advanced disease. METHODS: Baseline (pre-treatment) clinical, 2-dimensional echocardiogram with GLS and laboratory data were collected prospectively in 94 patients with newly diagnosed AL amyloidosis with rMayo stage III or IV disease. Overall survival (OS) was defined as time from baseline echocardiography to death. RESULTS: Of 94 patients, 60% (n = 56) had rMayo stage III and 40% (n = 38) had stage IV disease. Ninety of the 94 patients underwent plasma cell-directed therapy. The median left ventricular ejection fraction (LVEF) was 60%, and the median GLS was 13.2%. Of 94 patients, 64 died during follow-up. The median OS was 11.2 months, with an estimated 5-year OS of 21%. In univariable analysis, brain natriuretic peptides, GLS, LVEF, E/e' ratio, and rMayo stage were significantly associated with OS. In Cox regression, GLS provided incremental value over brain natriuretic peptide, troponin, and LVEF for predicting OS. Patients with GLS < -14.2% had a corresponding median OS and 5-year OS rate of 33.2 months and 39%, respectively, versus 7.7 months and 6% for those with GLS ≥ -14.2%. This difference was maintained despite further stratification by rMayo stage. CONCLUSIONS: Baseline GLS is an independent predictor of OS beyond the circulating biomarkers and can identify groups with different survival outcomes beyond the Mayo Staging.

Cardiovascular Complications Associated with Mediastinal Radiation.

PURPOSE OF REVIEW: Radiation-induced heart disease (RIHD) encompasses a broad range of pathologies and is a significant source of morbidity and mortality among cancer survivors. Increased awareness of the early and late consequences of mediastinal radiation has led to the development of strategies for cardiac risk reduction to improve outcomes through active surveillance and early detection of RIHD. This review aims to discuss the current knowledge on the presentation, diagnosis, and management of RIHD. RECENT FINDINGS: Decades' worth of cohort data demonstrates an increased risk of RIHD as cancer survivors age. Additionally, interventional/surgical management of irradiated patients poses unique considerations and can be technically challenging. Used in conjunction with echocardiography, multimodality imaging for morphologic and functional assessment adds complementary value in screening, surveillance, and targeted symptom investigation in patients at risk for RIHD. Furthermore, sensitive imaging parameters and biomarkers have shown potential in detecting subclinical RIHD. Despite the development of techniques which minimize cardiac exposure to ionizing radiation, their effects on the long-term development of RIHD remain to be seen. Due to the morbidity and mortality associated with RIHD, both patients and clinicians should be aware of the lifelong cardiovascular risks of mediastinal radiation exposure. RIHD surveillance should be a consideration throughout the survivorship period. Studies to evaluate the clinical consequences of contemporary radiation therapy strategies aimed at minimizing cardiac doses and the value of novel, more sensitive metrics for the early detection or prognostication of RIHD are ongoing.