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Daniel Royston

Analysis of Bone Marrow Fibrosis in Blood Cancer

A: Example of a fibrosis severity heatmap using our recently developed Continous Indexing of Fibrosis (CIF). B: Individual image tiles receive a CIF score from 0 to 1 depending on the severity of reticulin fibrosis, with high scores assigned to tiles displaying increased fiber quantity, thickness and intersections. C-D: This analysis improves the diagnosis of blood cancer subtypes (myeloproliferative neoplasms [MPN]) and allows high risk fibrotic features to be detected at early timepoints and tracked in individual patients.

Daniel Royston


Associate Professor of Pathology

  • Academic Pathologist
  • Consultant Haematopathologist

Quantiative Image analysis of blood cancers using AI / machine learning

Quantiative Image analysis of blood cancers using AI / machine learning

I am an academic Haematopathologist holding a joint University & NHS position. I trained in Medicine & Cellular Pathology before completing my DPhil at the University of Oxford in 2011.

The main focus of my research group is the systematic characterisation of the cellular and stromal interactions within biopsy material that drive the initiation and progression of myeloid blood cancers, with specific focus on myeloproliferative neoplasms (MPN). Our work uses cutting edge techniques in quantitative image analysis, including artificial intelligence (AI) / machine learning approaches. We have successfully demonstrated the utility of these approaches in clinical samples taken from patients, and offered new insights into the fundamental pathological processes driving disease progression and response to therapy. Our work is now employed in the evaluation of several new therapeutics in MPN, and provides clinical trialists with the first robust and objective measures of tissue-based disease modification. Understanding the spatial relationships of key cellular and stromal tissue components and their correlation to genetic and immune / inflammatory factors in myeloid diseases is now the key focus of my research group.

My research involves close multi-disciplinary collaborations between Cellular Pathology, Haematology and Biomedical Engineering (Institute of Biomedical Engineering; Professor Jens Rittscher). Our work is jointly funded by Blood Cancer UK (BCUK) and Cancer Research UK (CRUK).


Quantitative interpretation of bone marrow biopsies in MPN—What's the point in a molecular age? Ryou H, et al. BJH. 2023.

Continuous Indexing of Fibrosis (CIF): improving the assessment and classification of MPN patients. Ryou H, et al. Leukemia. 2022 Dec 5. doi: 10.1038/s41375-022-01773-0.

Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology. Royston D, et al. Hematol Oncol Clin North Am. 2021 Apr;35(2):279-293. doi: 10.1016/j.hoc.2021.01.002.

AI-Based Morphological Fingerprinting of Megakaryocytes: a New Tool for Assessing Disease in MPN Patients. Sirinukunwattana K, et al. Blood Adv. 2020 Jul 28;4(14):3284-3294. doi: 10.1182/bloodadvances.2020002230.

Perspective: Pivotal translational hematology and therapeutic insights in chronic myeloid hematopoietic stem cell malignancies. Mughal TI, et al. Hematol Oncol. 2022 Apr 3. doi: 10.1002/hon.2987. PMID: 35368098.

Multi-Scale Graphical Representation of Cell Environment. Theissen, et al. 2022; 3522-3525.10.1109/EMBC48229.2022.9871710.

Learning Cellular Phenotypes through Supervision. Theissen, et al, 2021. Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3592-3595. doi: 10.1109/EMBC46164.2021.9629898. PMID: 34892015.

In utero origin of myelofibrosis presenting in adult monozygotic twins. Sousos, et al. Nat Med. 2022 Jun;28(6):1207-1211. doi: 10.1038/s41591-022-01793-4.

Single-Cell Analyses Reveal Megakaryocyte-Biased Hematopoiesis in Myelofibrosis and Identify Mutant Clone-Specific Targets. Psaila B, et al. Mol Cell. 2020 May 7;78(3):477-492.e8. doi: 10.1016/j.molcel.2020.04.008.


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