Quantitative interpretation of bone marrow biopsies in MPN—What's the point in a molecular age?

Summary The diagnosis of myeloproliferative neoplasms (MPN) requires the integration of clinical, morphological, genetic and immunophenotypic findings. Recently, there has been a transformation in our understanding of the cellular and molecular mechanisms underlying disease initiation and progression in MPN. This has been accompanied by the widespread application of high‐resolution quantitative molecular techniques. By contrast, microscopic interpretation of bone marrow biopsies by haematologists/haematopathologists remains subjective and qualitative. However, advances in tissue image analysis and artificial intelligence (AI) promise to transform haematopathology. Pioneering studies in bone marrow image analysis offer to refine our understanding of the boundaries between reactive samples and MPN subtypes and better capture the morphological correlates of high‐risk disease. They also demonstrate potential to improve the evaluation of current and novel therapeutics for MPN and other blood cancers. With increased therapeutic targeting of diverse molecular, cellular and extra‐cellular components of the marrow, these approaches can address the unmet need for improved objective and quantitative measures of disease modification in the context of clinical trials. This review focuses on the state‐of‐the‐art in image analysis/AI of bone marrow tissue, with an emphasis on its potential to complement and inform future clinical studies and research in MPN. Quantitative analysis of bone marrow tissue features in myeloproliferative neoplasms (MPN) enables cellular and stromal components to be objectively assessed and compared across patient cohorts. This improved morphological feature analysis can inform and refine our understanding of the microenvironment in the marrow stem cell niche(s). Combining whole sample analysis with detailed cell–cell and cell–stromal interactions improves our disease modelling of MPN and provides a rational approach for improved diagnosis and disease classification. In turn, accurate and objective morphological correlates of therapeutic response can be developed that enable clinicians to monitor the response of individual patients in the context of large sample cohorts (cohort indexing).