New illustrated guide to bone marrow based on PT

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March 2022—CAP Publications will release this spring volume two of the second edition of the Color Atlas of Hematology—on bone marrow. It continues in the tradition of its predecessor on peripheral blood cells (volume one, 2018): morphologic identification of cells based on proficiency testing. The senior and associate editors have organized the various components of volume two into “an eminently readable, practical, and in many respects entertaining resource for anyone interested in bone marrow morphology, physiology, and pathophysiology,” Donald S. Karcher, MD, of George Washington University, writes in the foreword.

CAP TODAY asked Eric F. Glassy, MD, senior editor of the atlas and medical director of Affiliated Pathologists Medical Group, Rancho Dominguez, Calif., about the newest volume. Here is what he told us. A brief section from the atlas appears below.

Tell us about the book’s content and how it’s organized and why Donald Karcher, MD, wrote in the foreword that the book is “more than a mere atlas.”
Like its predecessor, volume two of the second edition of the Color Atlas of Hematology is based on proficiency testing challenges. That is its origin story. Volume one focused on peripheral blood and volume two on bone marrow. After an introduction containing sections on bone marrow sampling, marrow environment, smear differentials, and artificial intelligence, the main chapters deal with nucleated red cells, granulocytes and monocytes, megakaryocytic cells, blasts, lymphocytes and plasma cells, and miscellaneous bone marrow cells. Each identification has vital statistics, illustrations highlighting pertinent morphologic features, a discussion, and proficiency testing photomicrographs. Sections called “A Closer Look At . . .” provide a deeper dive into important concepts. But there is so much more.

Dr. Karcher rightly points out that this book is not just an atlas. Of course there are plenty of static images (937) and illustrations (274). More importantly, there are 125 virtual bone marrow smear links that can be navigated using the CAP’s DigitalScope whole slide image viewer. These virtual smears provide as close to a real glass slide and analog microscope experience as possible, in keeping with one of the key tenets of proficiency testing. Finally, authors recorded 16 video vignettes of topics they were passionate about. This supplements the text quite nicely and follows the success of the CAP’s Virtual Lecture series.

The images of bone marrow elements in the new book are taken from photographs used over many years in the CAP proficiency testing challenges. And you write in the preface that the “collective observations and wisdom of thousands of laboratorians are used to define truth.” Why is this one of the book’s strengths?
Morphology is still foundational for hematology. It provides diagnoses well before sophisticated testing can be performed, such as flow cytometry, cytogenetics, and FISH. But experts can disagree. The CAP Hematology/Clinical Microscopy Committee—the group that selects the proficiency testing challenges—does not always have a consensus for cell identification. Blood cells are dynamic—morphologic features blend from one stage to another. So how do you determine true north? What is the correct answer? The committee has always believed in the power of the laboratory—the collective wisdom of pathologists and technologists. The final answer is built on a crowdsourced response to an unknown cell. That is how proficiency testing is graded—80 percent of labs are needed for consensus.

The section on artificial intelligence in the introduction envisions a future world where digital pathology coupled with artificial intelligence transform the practice of hematopathology. Can you tell us a bit more?
This section in the introductory chapter was written by a good friend, Mohamed Salama, MD. He predicts that artificial intelligence will dramatically change the practice of hematology over the next five to 10 years. Algorithms combined with digital pathology will soon outperform traditional microscopy. Multiple studies using machine learning for diagnosis, classification, and differentiation of neoplastic hematologic conditions and their precursors have confirmed the utility of AI in the diagnostic workflow. This will most likely change how proficiency testing is performed, and if that is the case, I am sure the next edition of this Color Atlas will have a greatly expanded discussion of hematologic algorithms.