{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T03:40:06Z","timestamp":1792813206827,"version":"4.5.0"},"reference-count":25,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82473728"],"award-info":[{"award-number":["82473728"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82200151"],"award-info":[{"award-number":["82200151"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Br J Haematol"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>The subjectivity of morphological assessment and the overlapping pathological features of different subtypes of myeloproliferative neoplasms (MPNs) make accurate diagnosis challenging. To improve the pathological assessment of MPNs, we developed a diagnosis model (fusion model) based on the combination of bone marrow whole\u2010slide images (deep learning [DL] model) and clinical parameters (clinical model). Thousand and fifty\u2010one MPN and non\u2010MPN patients were divided into the training, internal testing and one internal and two external validation cohorts (the combined validation cohort). In the combined validation cohort, fusion model achieved higher areas under curve (AUCs) than clinical or DL model or both for MPNs and subtype identification. Compared with haematopathologists with different experience, clinical model achieved AUC\u00a0which was comparable to seniors and higher than juniors (<jats:italic>p<\/jats:italic>\u2009=\u20090.0208) for polycythaemia vera. The AUCs of fusion model were comparable to seniors and higher than juniors for essential thrombocytosis (<jats:italic>p<\/jats:italic>\u2009=\u20090.0141), prefibrotic primary myelofibrosis (<jats:italic>p<\/jats:italic>\u2009=\u20090.0085) and overt primary myelofibrosis (<jats:italic>p<\/jats:italic>\u2009=\u20090.0330) identification. In conclusion, the performances of our proposed models are equivalent to senior haematopathologists and better than juniors, providing a new perspective on the utilization of DL algorithms in MPN morphological assessment.<\/jats:p>","DOI":"10.1111\/bjh.19938","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T06:50:23Z","timestamp":1733899823000},"page":"596-606","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology"],"prefix":"10.1111","volume":"206","author":[{"given":"Rong","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Haematology, Collaborative Innovation Center for Cancer Personalized Medicine Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4000-8210","authenticated-orcid":false,"given":"Zhongxun","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Haematology, Collaborative Innovation Center for Cancer Personalized Medicine Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Ministry of Education  Nanjing China"},{"name":"Department of Biostatistics, Center for Global Health, School of Public Health Nanjing Medical University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangmin","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Center for Global Health, School of Public Health Nanjing Medical University  Nanjing China"},{"name":"Department of Public Health, School of Medicine &amp; 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