Current Challenges in the Characterization of Myeloid Neoplasms

Abstract

We are fortunate to live in unprecedented times, when rapid progress in molecular diagnostic techniques and computational biology has allowed tremendous advances in scientific research of numerous human conditions, including in the field of bone marrow pathology, leading to a more granular understanding of myeloid neoplasms than ever before. It is therefore not surprising that the exponential proliferation of scientific reports, the sheer volume and the variable quality of the new information have led to significant new questions and unresolved concerns. One of the great challenges we now face is to how best summarize and make sense of the abundant and occasionally conflicting scientific data. The field of hematopathology in particular had an unfortunate recent fracturing where most of the editors and authors of the 3rd, 4th, and the updated 4th edition of the World Health Organization (WHO) Classification of Tumors of Hematopoietic and Lymphoid Tissues have organized a new classificationthe International Consensus Classification (ICC) [1], while the upcoming 5th edition of the WHO is led by a different group of expert hematopathologists [2]. The two classifications are generally in agreement, but there are occasional significant differences, especially in the area of bone marrow pathology to which this issue is dedicated. Thus, this very timely special issue in Pathobiology has been sponsored by the European Bone Marrow Working Group and is dedicated to updates and current challenges in the diagnosis of myeloid neoplasms.Artificial intelligence is an extremely timely topic with prominent coverage in the international news cycle. In particular, advances in digital pathology continue to