Year: 2025
Type: article
Abstract: 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.
Source: eJHaem
DOI: 10.1002/jha2.70005
Authors: Hosuk Ryou, Emily Thomas, Marta Wojciechowska, Laura Harding, Ka Ho Tam, Ruoyu Wang, Xuezi Hu, Jens Rittscher, Rosalin Cooper, Daniel Royston
Language: English
Subfield: Genetics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,860
OpenAlex ID: W4408070067