Reticulin‐Free Quantitation of Bone Marrow Fibrosis in MPNs: Utility and Applications

Journal article

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.

Authors: Ryou, H; Thomas, E; Wojciechowska, M; Harding, L; Tam, KH

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: eJHaem
• Volume: 6
• Issue: 2
• Article number: e70005
• Publication date: 2025-02-27
• Acceptance date: 2025-01-28
• DOI: 10.1002/jha2.70005
• EISSN: 2688-6146
• ISSN: 2688-6146
• Language: English
• Keywords: myeloproliferative disease; diagnostic haematology; machine learning; bone marrow pathology; marrow fibrosis; haematological malignancy