Barosi G, Mesa RA, Thiele J, Cervantes F, Campbell PJ, Verstovsek S, et al. Proposed criteria for the diagnosis of post-polycythemia vera and post-essential thrombocythemia myelofibrosis: a consensus statement from the International Working Group for Myelofibrosis Research and Treatment. Leukemia. 2008;22:437–8.
Google Scholar
Thiele J, Georgii A, Vykoupil KF. Ultrastructure of chronic megakaryocytic-granulocytic myelosis. Blut. 1976;32:433–8.
Google Scholar
Vardiman JW, Harris NL, Brunning RD. The World Health Organization (WHO) classification of the myeloid neoplasms. Blood. 2002;100:2292–302.
Google Scholar
Vardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz MJ, Porwit A, et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood. 2009;114:937–51.
Google Scholar
Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127:2391–405.
Google Scholar
Khoury JD, Solary E, Abla O, Akkari Y, Alaggio R, Apperley JF, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. Leukemia. 2022;36:1703–19.
Google Scholar
Rumi E, Boveri E, Bellini M, Pietra D, Ferretti VV, Sant’Antonio E, et al. Clinical course and outcome of essential thrombocythemia and prefibrotic myelofibrosis according to the revised WHO 2016 diagnostic criteria. Oncotarget. 2017;8:101735–44.
Google Scholar
Barbui T, Thiele J, Passamonti F, Rumi E, Boveri E, Ruggeri M, et al. Survival and disease progression in essential thrombocythemia are significantly influenced by accurate morphologic diagnosis: an international study. J Clin Oncol. 2011;29:3179–84.
Google Scholar
Guglielmelli P, Pacilli A, Rotunno G, Rumi E, Rosti V, Delaini F, et al. Presentation and outcome of patients with 2016 WHO diagnosis of prefibrotic and overt primary myelofibrosis. Blood. 2017;129:3227–36.
Google Scholar
Gisslinger H, Jeryczynski G, Gisslinger B, Wolfler A, Burgstaller S, Buxhofer-Ausch V, et al. Clinical impact of bone marrow morphology for the diagnosis of essential thrombocythemia: comparison between the BCSH and the WHO criteria. Leukemia. 2016;30:1126–32.
Google Scholar
Finazzi G, Carobbio A, Thiele J, Passamonti F, Rumi E, Ruggeri M, et al. Incidence and risk factors for bleeding in 1104 patients with essential thrombocythemia or prefibrotic myelofibrosis diagnosed according to the 2008 WHO criteria. Leukemia. 2012;26:716–9.
Google Scholar
Passamonti F, Rumi E, Arcaini L, Boveri E, Elena C, Pietra D, et al. Prognostic factors for thrombosis, myelofibrosis, and leukemia in essential thrombocythemia: a study of 605 patients. Haematologica. 2008;93:1645–51.
Google Scholar
Arber DA, Orazi A, Hasserjian RP, Borowitz MJ, Calvo KR, Kvasnicka HM, et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140:1200–28.
Google Scholar
Madelung AB, Bondo H, Stamp I, Lovgreen P, Nielsen SL, Falensteen A, et al. WHO classification 2008 of myeloproliferative neoplasms: a workshop learning effect-the Danish experience. APMIS. 2015;123:787–92.
Google Scholar
Thiele J, Kvasnicka HM, Mullauer L, Buxhofer-Ausch V, Gisslinger B, Gisslinger H. Essential thrombocythemia versus early primary myelofibrosis: a multicenter study to validate the WHO classification. Blood. 2011;117:5710–8.
Google Scholar
Srisuwananukorn A, Salama ME, Pearson AT. Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary. Haematologica. 2023;108:1993–2010.
Google Scholar
Srisuwananukorn A, Krull JE, Ma Q, Zhang P, Pearson AT, Hoffman R. Applications of artificial intelligence to myeloproliferative neoplasms: a narrative review. Expert Rev Hematol. 2024;17:1–9.
He K, Zhang X, Ren S, Sun J. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV) 2015 1026–34 https://doi.org/10.1109/iccv.2015.123.
Perez-Lopez R, Ghaffari Laleh N, Mahmood F, Kather JN. A guide to artificial intelligence for cancer researchers. Nat Rev Cancer. 2024;24:427–41.
Google Scholar
Rashidi HH, Pantanowitz J, Chamanzar A, Fennell B, Wang Y, Gullapalli RR, et al. Generative Artificial Intelligence in Pathology and Medicine: A Deeper Dive. Mod Pathol. 2025;38:100687.
Google Scholar
Campanella G, Chen S, Singh M, Verma R, Muehlstedt S, Zeng J, et al. A clinical benchmark of public self-supervised pathology foundation models. Nat Commun. 2025;16:3640.
Google Scholar
Ryou H, Sirinukunwattana K, Aberdeen A, Grindstaff G, Stolz BJ, Byrne H, et al. Continuous Indexing of Fibrosis (CIF): improving the assessment and classification of MPN patients. Leukemia. 2023;37:348–58.
Google Scholar
Dolezal JM, Wolk R, Hieromnimon HM, Howard FM, Srisuwananukorn A, Karpeyev D, et al. Deep Learning Generates Synthetic Cancer Histology for Explainability and Education, 2022 November 01, 2022:[arXiv:2211.06522 p.]. Available from: https://ui.adsabs.harvard.edu/abs/2022arXiv221106522D.
Hieromnimon HM, Dolezal J, Doytcheva K, Howard FM, Kochanny S, Zhang Z, et al. Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine. Genome Medicine. 2025;4:38.
Hieromnimon HM, Trzcinska A, Wen FT, Howard FM, Dolezal JM, Dyer E, et al. Analysis of AI foundation model features decodes the histopathologic landscape of HPV-positive head and neck squamous cell carcinomas. Oral Oncol. 2025;163:107207.
Google Scholar
Dolezal JM, Kochanny S, Dyer E, Ramesh S, Srisuwananukorn A, Sacco M, et al. Slideflow: deep learning for digital histopathology with real-time whole-slide visualization. BMC Bioinformatics. 2024;25:134.
Google Scholar
Center OS. Ohio Supercomputer Center. Ohio Supercomputer Center; 1987.
Mongan J, Moy L, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiology: Artificial Intelligence. 2020;2:e200029.
Google Scholar
Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11:e048008.
Google Scholar
Wang X, Du Y, Yang S, Zhang J, Wang M, Zhang J, et al. RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval. Med Image Anal. 2023;83:102645.
Google Scholar
Ilse M, Tomczak J, Welling M. Attention-based deep multiple instance learning. Proceedings of the 35th International Conference on Machine Learning; 10-15 July 2018; Stockholm, Sweden: PMLR; 2018;80:2127–36.
Bankhead P, Loughrey MB, Fernandez JA, Dombrowski Y, McArt DG, Dunne PD, et al. QuPath: Open source software for digital pathology image analysis. Sci Rep. 2017;7:16878.
Google Scholar
Palomaki VA, Koivukangas V, Merilainen S, Lehenkari P, Karttunen TJ. A Straightforward Method for Adipocyte Size and Count Analysis Using Open-source Software QuPath. Adipocyte. 2022;11:99–107.
Google Scholar
Dolezal JM, Srisuwananukorn A, Karpeyev D, Ramesh S, Kochanny S, Cody B, et al. Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology. Nature Communications 2022;13:6572.
Barbui T, Thiele J, Vannucchi AM, Tefferi A. Problems and pitfalls regarding WHO-defined diagnosis of early/prefibrotic primary myelofibrosis versus essential thrombocythemia. Leukemia. 2013;27:1953–8.
Google Scholar
Carobbio A, Finazzi G, Thiele J, Kvasnicka HM, Passamonti F, Rumi E, et al. Blood tests may predict early primary myelofibrosis in patients presenting with essential thrombocythemia. Am J Hematol. 2012;87:203–4.
Google Scholar
Schalling M, Gleiss A, Gisslinger B, Wolfler A, Buxhofer-Ausch V, Jeryczynski G, et al. Essential thrombocythemia vs. pre-fibrotic/early primary myelofibrosis: discrimination by laboratory and clinical data. Blood Cancer J. 2017;7:643.
Google Scholar
Lekovic D, Bogdanovic A, Sobas M, Arsenovic I, Smiljanic M, Ivanovic J, et al. Easily Applicable Predictive Score for Differential Diagnosis of Prefibrotic Primary Myelofibrosis from Essential Thrombocythemia. Cancers (Basel). 2023;15:4180.
Harrison CN, Campbell PJ, Buck G, Wheatley K, East CL, Bareford D, et al. Hydroxyurea compared with anagrelide in high-risk essential thrombocythemia. N Engl J Med. 2005;353:33–45.
Google Scholar
Rozman C, Cervantes F, Rozman M, Mercader JM, Montserrat E. Magnetic resonance imaging in myelofibrosis and essential thrombocythaemia: contribution to differential diagnosis. Br J Haematol. 1999;104:574–80.
Google Scholar
Lu M, Xia L, Liu YC, Hochman T, Bizzari L, Aruch D, et al. Lipocalin produced by myelofibrosis cells affects the fate of both hematopoietic and marrow microenvironmental cells. Blood. 2015;126:972–82.
Google Scholar
