Cardiac magnetic resonance imaging characterization of acute rejection in a porcine heterotopic heart transplantation model.

Preclinical disease models are important for the advancement of therapeutics towards human clinical trials. One of the difficult tasks of developing a well-characterized model is having a reliable modality with which to trend the progression of disease. Acute rejection is one of the most devastating complications that can occur following organ transplantation. Specifically in cardiac transplantation, approximately 12% of patients will experience at least one episode of moderate or severe acute rejection in the first year. Currently, the gold standard for monitoring rejection in the clinical setting is to perform serial endomyocardial biopsies for direct histological assessment. However, this is difficult to reproduce in a porcine model of acute rejection in cardiac transplantation where the heart is heterotopically transplanted in an abdominal position. Cardiac magnetic resonance imaging is arising as an alternative for serial screening for acute rejection in cardiac transplantation. This is an exploratory study to create and define a standardized cardiac magnetic resonance screening protocol for characterizing changes associated with the presence of acute rejection in this preclinical model of disease. Results demonstrate that increases in T1 mapping, T2 mapping, left ventricular mass, and in late gadolinium enhancement are significantly correlated with presence of acute rejection.

Ultrastructural cardiac pathology: the wide (yet so very small) world of cardiac electron microscopy.

Electron microscopy (EM) was a popular diagnostic tool in the 1970s and early 80s. With the adoption of newer, less expensive techniques, such as immunohistochemistry, the role of EM in diagnostic surgical pathology has dwindled substantially. Nowadays, even in academic centers, EM interpretation is relegated to renal pathologists and the handful of (aging) pathologists with experience using the technique. As such, EM interpretation is truly arcane-understood by few and mysterious to many. Nevertheless, there remain situations in which EM is the best or only ancillary test to ascertain a specific diagnosis. Thus, there remains a critical need for the younger generation of surgical pathologists to learn EM interpretation. Recognizing this need, cardiac EM was made the theme of the Cardiovascular Evening Specialty Conference at the 2023 United States and Canadian Academy of Pathology (USCAP) annual meeting in New Orleans, Louisiana. Each of the speakers contributed their part to this article, the purpose of which is to review EM as it pertains to myocardial tissue and provide illustrative examples of the spectrum of ultrastructural cardiac pathology seen in storage/metabolic diseases, cardiomyopathies, infiltrative disorders, and cardiotoxicities.

A machine learning algorithm improves the diagnostic accuracy of the histologic component of antibody mediated rejection (AMR-H) in cardiac transplant endomyocardial biopsies.

BACKGROUND: Pathologic antibody mediated rejection (pAMR) remains a major driver of graft failure in cardiac transplant patients. The endomyocardial biopsy remains the primary diagnostic tool but presents with challenges, particularly in distinguishing the histologic component (pAMR-H) defined by 1) intravascular macrophage accumulation in capillaries and 2) activated endothelial cells that expand the cytoplasm to narrow or occlude the vascular lumen. Frequently, pAMR-H is difficult to distinguish from acute cellular rejection (ACR) and healing injury. With the advent of digital slide scanning and advances in machine deep learning, artificial intelligence technology is widely under investigation in the areas of oncologic pathology, but in its infancy in transplant pathology. For the first time, we determined if a machine learning algorithm could distinguish pAMR-H from normal myocardium, healing injury and ACR. MATERIALS AND METHODS: A total of 4,212 annotations (1,053 regions of normal, 1,053 pAMR-H, 1,053 healing injury and 1,053 ACR) were completed from 300 hematoxylin and eosin slides scanned using a Leica Aperio GT450 digital whole slide scanner at 40X magnification. All regions of pAMR-H were annotated from patients confirmed with a previous diagnosis of pAMR2 (>50% positive C4d immunofluorescence and/or >10% CD68 positive intravascular macrophages). Annotations were imported into a Python 3.7 development environment using the OpenSlide™ package and a convolutional neural network approach utilizing transfer learning was performed. RESULTS: The machine learning algorithm showed 98% overall validation accuracy and pAMR-H was correctly distinguished from specific categories with the following accuracies: normal myocardium (99.2%), healing injury (99.5%) and ACR (99.5%). CONCLUSION: Our novel deep learning algorithm can reach acceptable, and possibly surpass, performance of current diagnostic standards of identifying pAMR-H. Such a tool may serve as an adjunct diagnostic aid for improving the pathologist's accuracy and reproducibility, especially in difficult cases with high inter-observer variability. This is one of the first studies that provides evidence that an artificial intelligence machine learning algorithm can be trained and validated to diagnose pAMR-H in cardiac transplant patients. Ongoing studies include multi-institutional verification testing to ensure generalizability.

The over diagnosis of diffuse mesothelioma: An analysis of 311 cases with recommendations for the avoidance of pitfalls.

BACKGROUND: The diagnosis of mesothelioma may be challenging. We investigated a large database of cases in order to determine the frequency with which a diagnosis of mesothelioma was made incorrectly and the most frequent causes of error. DESIGN: A database including more than 4000 consultation cases of histologically confirmed mesothelioma was examined to identify cases in which mesothelioma was diagnosed by at least one pathologist when the available information pointed towards a different diagnosis. RESULTS: There were 311 cases misdiagnosed as mesothelioma. The most common category was metastatic carcinoma to the pleura or peritoneum (129 cases: 73 lung carcinomas, 15 renal cell carcinomas). The next most common category was primary lung cancer (111 cases: 55 sarcomatoid carcinoma, 56 pseudomesotheliomatous carcinoma). The third most common category was primary malignancies arising from or near the serosal membranes (33 cases). The fourth most common category was fibrous pleurisy (38 cases). The most common errors were failure to consider important radiographic information regarding the gross distribution of tumor, lack of awareness or consideration of another malignancy, overreliance on certain immunohistochemical results, and failure to perform certain diagnostic histochemical, immunohistochemical, or ultrastructural studies. CONCLUSIONS: There are a number of diagnostic pitfalls that can lead to the over diagnosis of mesothelioma. Careful attention to clinical and radiographic information as well as performance of appropriate ancillary tests can help to prevent such misdiagnoses. Detailed examples will be presented to assist in the avoidance of these pitfalls with emphasis on the most commonly observed errors.