@misc{ryou_continuous_2022, abstract = {The detection and grading of fibrosis in myeloproliferative neoplasms ({MPN}) is an important component of disease classification, prognostication and disease monitoring. However, current fibrosis grading systems are only semi-quantitative and fail to capture sample heterogeneity. To improve the detection, quantitation and representation of reticulin fibrosis, we developed a machine learning ({ML}) approach using bone marrow trephine ({BMT}) samples (n = 107) from patients diagnosed with {MPN} or a reactive / nonneoplastic marrow. The resulting Continuous Indexing of Fibrosis ({CIF}) enhances the detection and monitoring of fibrosis within {BMTs}, and aids the discrimination of {MPN} subtypes. When combined with megakaryocyte feature analysis, {CIF} discriminates between the frequently challenging differential diagnosis of essential thrombocythemia ({ET}) and pre-fibrotic myelofibrosis (pre-{PMF}) with high predictive accuracy [area under the curve = 0.94]. {CIF} also shows significant promise in the identification of {MPN} patients at risk of disease progression; analysis of samples from 35 patients diagnosed with {ET} and enrolled in the Primary Thrombocythemia-1 ({PT}-1) trial identified features predictive of post-{ET} myelofibrosis (area under the curve = 0.77). In addition to these clinical applications, automated analysis of fibrosis has clear potential to further refine disease classification boundaries and inform future studies of the micro-environmental factors driving disease initiation and progression in {MPN} and other stem cell disorders. The image analysis methods used to generate {CIF} can be readily integrated with those of other key morphological features in {MPNs}, including megakaryocyte morphology, that lie beyond the scope of conventional histological assessment. Key {PointsMachine} learning enables an objective and quantitative description of reticulin fibrosis within the bone marrow of patients with myeloproliferative neoplasms ({MPN}),Automated analysis and Continuous Indexing of Fibrosis ({CIF}) captures heterogeneity within {MPN} samples and has utility in refined classification and disease {monitoringQuantitative} fibrosis assessment combined with topological data analysis may help to predict patients at increased risk of progression to post-{ET} myelofibrosis, and assist in the discrimination of {ET} and pre-fibrotic {PMF} (pre-{PMF})}, author = {Ryou, Hosuk and Sirinukunwattana, Korsuk and Aberdeen, Alan and Grindstaff, Gillian and Stolz, Bernadette and Byrne, Helen and Harrington, Heather A. and Sousos, Nikolaos and Godfrey, Anna L. and Harrison, Claire N. and Psaila, Bethan and Mead, Adam J. and Rees, Gabrielle and Turner, Gareth D. H. and Rittscher, Jens and Royston, Daniel}, date = {2022-06-06}, doi = {10.1101/2022.06.06.22276014}, keywords = {1 - Fibrosis, 1 - Medicine, 1 - Neoplasm, 2 - Machine learning, 2 - Persistent homology, 3 - 2D images, 3 - Tile scores, Innovate}, langid = {english}, note = {Pages: 2022.06.06.22276014}, publisher = {{medRxiv}}, rights = {© 2022, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.}, shorttitle = {Continuous Indexing of Fibrosis ({CIF})}, title = {Continuous Indexing of Fibrosis ({CIF}): Improving the Assessment and Classification of {MPN} Patients}, url = {https://www.medrxiv.org/content/10.1101/2022.06.06.22276014v1}, urldate = {2022-07-08} }