Artificial intelligence-based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients.

ABSTRACT: Accurate diagnosis and classification of myeloproliferative neoplasms (MPNs) requires integration of clinical, morphological, and genetic findings. Despite major advances in our understanding of the molecular and genetic basis of MPNs, the morphological assessment of bone marrow trephines (BMT) is critical in differentiating MPN subtypes and their reactive mimics. However, morphological assessment is heavily constrained by a reliance on subjective, qualitative, and poorly reproducible criteria. To improve the morphological assessment of MPNs, we have developed a machine learning approach for the automated identification, quantitative analysis, and abstract representation of megakaryocyte features using reactive/nonneoplastic BMT samples (n = 43) and those from patients with established dia

SUBMITTER: Sirinukunwattana K 

PROVIDER: S-EPMC7391156 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature