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BACKGROUND: Automated quantitation of marrow fibrosis promises to improve fibrosis assessment in myeloproliferative neoplasms (MPNs). However, analysis of reticulin-stained images is complicated by technical challenges within laboratories and variability between institutions. METHODS: We have developed a machine learning model that can quantitatively assess fibrosis directly from H&E-stained bone marrow trephine tissue sections. RESULTS: Our haematoxylin and eosin (H&E)-based fibrosis quantitation model demonstrates comparable performance to an existing reticulin-stained model (Continuous Indexing of Fibrosis [CIF]) while benefitting from the improved tissue retention and staining characteristics of H&E-stained sections. CONCLUSIONS: H&E-derived quantitative marrow fibrosis has potential to augment routine practice and clinical trials while supporting the emerging field of spatial multi-omic analysis.

Original publication

DOI

10.1002/jha2.70005

Type

Journal

EJHaem

Publication Date

04/2025

Volume

6

Keywords

bone marrow pathology, diagnostic haematology, haematological malignancy, machine learning, marrow fibrosis, myeloproliferative disease