ModPath Chat Modern Pathology

A most exciting conversation with some of the many engaged listeners of our podcast around the globe. Our host is joined by Dr. Zeeshan Ansar from Karachi, Pakistan; Dr. Emilian Olteanu, from Timisoara, Romania; Dr. Sanam Loghavi from Houston, USA and Dr. Sanjay Mukhopadhyay, from Clevland, USA.

In this episode, our host and Dr. Lisa Rooper from Johns Hopkins University, on behalf of her group of esteemed coauthors, discuss their recent study on the molecular characteristics of “Olfactory Carcinoma.” Targeted molecular profiling of 23 cases of the rare sinonasal tumor was performed to help clarify their pathogenesis and classification. The authors found recurrent Wnt pathway and ARID1A alterations, suggesting that sinonasal neuroendocrine and epithelial tumors may be best regarded as a histologic and molecular spectrum.

Integration of molecular analyses in routine diagnostics is the new practice paradigm of surgical pathology practice. The 2024 USCAP Long Course led by Dr. John Hart from the University of Chicago and Daniela Allende of Cleveland Clinic focuses on hepatic neoplasms and medical diseases where there are opportunities for ancillary molecular testing to refine the diagnosis and provide clinically relevant prognostic information. In this episode, the guests offer a preview of the long course sessions encompassing the utility of germline testing to identify liver disease risk alleles and the exciting potential of emerging technologies.

In this meet the expert episode, ModPath CHAT host Dr. George Netto discusses with Dr. Liron Pantanowitz, Chair of Pathology at UPMC and a pioneer leader in the field, his views on the current status and future direction of Digital Pathology. The conversation touches the various aspects of the digital transformation journey in AP, from infrastructure requirement to talent acquisition and training, regulatory hurdles and expectation of financial return on investments.

Flow cytometric analysis of blood & bone marrow for diagnosis of acute myelogenous leukemia (AML) relies heavily on manual intervention in the processing & analysis steps. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions & generate interpretable insights regarding the classification of a sample from individual events.
The Drs. Olga Pozdnyakova and Joshua Lewis discuss their newly developed computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. The study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis & molecular characterization.

Modern Pathology is publishing a seminal series of review articles highlighting the recently completed fifth edition of the World Health Organization classification of hematolymphoid tumors (WHO-HEM5). In this episode of ModPath CHAT, Dr. Sanam Loghavi from The MD Anderson Cancer Center in Houston, discusses the major updates in the classification of myeloid neoplasms, providing a comparison with WHO-HEM4R, and offering guidance on how the new classification can be applied to the diagnosis of myeloid neoplasms in routine practice.