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

EJHaem Pub Date : 2025-02-27 DOI:10.1002/jha2.70005

Hosuk Ryou, Emily Thomas, Marta Wojciechowska, Laura Harding, Ka Ho Tam, Ruoyu Wang, Xuezi Hu, Jens Rittscher, Rosalin Cooper, Daniel Royston

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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.

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无网状蛋白定量骨髓纤维化在mpn：效用和应用

骨髓纤维化的自动定量测定有望改善骨髓增生性肿瘤（mpn）的纤维化评估。然而，由于实验室内部的技术挑战和机构之间的差异，网状蛋白染色图像的分析变得复杂。我们开发了一种机器学习模型，可以直接从H&； e染色的骨髓环钻组织切片定量评估纤维化。我们基于血红素和伊红（H&；E）的纤维化定量模型的性能与现有的网状蛋白染色模型（连续纤维化指数[CIF]）相当，同时受益于H&；E染色切片改善的组织保留和染色特性。结论：H&； e来源的定量骨髓纤维化具有增强常规实践和临床试验的潜力，同时支持新兴的空间多组学分析领域。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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EJHaem

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