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Journal article

Reticulin-free quantitation of bone marrow fibrosis in MPNs: utility and applications

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.
Publication status:
Published
Peer review status:
Peer reviewed

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Files:
Publisher copy:
10.1002/jha2.70005

Authors

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Institution:
University of Oxford
Division:
MSD
Department:
Radcliffe Department of Medicine
Sub department:
RDM-Nuffield Division of Clinical Laboratory Sciences
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Big Data Institute
Role:
Author
ORCID:
0000-0002-4563-8695
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Big Data Institute
Role:
Author


More from this funder
Funder identifier:
https://ror.org/0055acf80
Grant:
23012
More from this funder
Funder identifier:
https://ror.org/054225q67
Grant:
EDDPJT-May23/100034
More from this funder
Funder identifier:
https://ror.org/0439y7842
Grant:
EP/M013774/1
More from this funder
Funder identifier:
https://ror.org/01e473h50


Publisher:
Wiley
Journal:
eJHaem More from this journal
Volume:
6
Issue:
2
Article number:
e70005
Place of publication:
United States
Publication date:
2025-02-27
Acceptance date:
2025-01-28
DOI:
EISSN:
2688-6146
Pmid:
40017714


Language:
English
Keywords:
Pubs id:
2093076
Local pid:
pubs:2093076
Deposit date:
2025-04-24
ARK identifier:

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