{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:49:28Z","timestamp":1792784968115,"version":"4.5.0"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Grants-in Aid for Scientific Research","doi-asserted-by":"crossref","award":["20K20230"],"award-info":[{"award-number":["20K20230"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001691","name":"Grants-in Aid for Scientific Research","doi-asserted-by":"crossref","award":["18K07424"],"award-info":[{"award-number":["18K07424"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"crossref"}]},{"name":"International Joint Research Programs","award":["19KK0221"],"award-info":[{"award-number":["19KK0221"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis are characterized by abnormal proliferation of mature bone marrow cell lineages. Since various non-hematologic disorders can also cause leukocytosis, thrombocytosis and polycythemia, the detection of abnormal peripheral blood cells is essential for the diagnostic screening of Ph-negative MPNs. We sought to develop an automated diagnostic support system of Ph-negative MPNs. Our strategy was to combine the complete blood cell count and research parameters obtained by an automated hematology analyzer (Sysmex XN-9000) with morphological parameters that were extracted using a convolutional neural network deep learning system equipped with an Extreme Gradient Boosting (XGBoost)-based decision-making algorithm. The developed system showed promising performance in the differentiation of PV, ET, and MF with high accuracy when compared with those of the human diagnoses, namely:\u2009&gt;\u200990% sensitivity and\u2009&gt;\u200990% specificity. The calculated area under the curve of the ROC curves were 0.990, 0.967, and 0.974 for PV, ET, MF, respectively. This study is a step toward establishing a universal automated diagnostic system for all types of hematology disorders.<\/jats:p>","DOI":"10.1038\/s41598-021-82826-9","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T16:23:32Z","timestamp":1612887812000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen"],"prefix":"10.1038","volume":"11","author":[{"given":"Konobu","family":"Kimura","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomohiko","family":"Ai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Horiuchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akihiko","family":"Matsuzaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kumiko","family":"Nishibe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Setsuko","family":"Marutani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaori","family":"Saito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kimiko","family":"Kaniyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ikki","family":"Takehara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kinya","family":"Uchihashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akimichi","family":"Ohsaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoko","family":"Tabe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,2,9]]},"reference":[{"key":"82826_CR1","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1038\/nature03546","volume":"434","author":"C James","year":"2005","unstructured":"James, C. et al. A unique clonal JAK2 mutation leading to constitutive signalling causes polycythaemia vera. Nature 434, 1144\u20131148. https:\/\/doi.org\/10.1038\/nature03546 (2005).","journal-title":"Nature"},{"key":"82826_CR2","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1002\/ajh.24221","volume":"91","author":"A Tefferi","year":"2016","unstructured":"Tefferi, A. Myeloproliferative neoplasms: A decade of discoveries and treatment advances. Am. J. Hematol. 91, 50\u201358. https:\/\/doi.org\/10.1002\/ajh.24221 (2016).","journal-title":"Am. J. Hematol."},{"key":"82826_CR3","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.6004\/jnccn.2017.0157","volume":"15","author":"RA Mesa","year":"2017","unstructured":"Mesa, R. A. et al. NCCN guidelines insights: Myeloproliferative neoplasms, version 2.2018. J. Natl. Compr. Cancer Netw. 15, 1193\u20131207. https:\/\/doi.org\/10.6004\/jnccn.2017.0157 (2017).","journal-title":"J. Natl. Compr. Cancer Netw."},{"key":"82826_CR4","doi-asserted-by":"publisher","first-page":"2475","DOI":"10.1182\/blood-2017-06-782037","volume":"130","author":"J Nangalia","year":"2017","unstructured":"Nangalia, J. & Green, A. R. Myeloproliferative neoplasms: From origins to outcomes. Blood 130, 2475\u20132483. https:\/\/doi.org\/10.1182\/blood-2017-06-782037 (2017).","journal-title":"Blood"},{"key":"82826_CR5","unstructured":"Swerdlow SH, C. E., Harris NL, Jaffe ES, Pileri SA, Stein H, Thiele J. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. Revised Fourth Edition. (World Health Organization, 2017)."},{"key":"82826_CR6","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s11899-018-0453-y","volume":"13","author":"F Passamonti","year":"2018","unstructured":"Passamonti, F., Mora, B., Barraco, D. & Maffioli, M. Post-ET and post-PV myelofibrosis: Updates on a distinct prognosis from primary myelofibrosis. Curr. Hematol. Malig. Rep. 13, 173\u2013182. https:\/\/doi.org\/10.1007\/s11899-018-0453-y (2018).","journal-title":"Curr. Hematol. Malig. Rep."},{"issue":"Suppl 1","key":"82826_CR7","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1111\/ijlh.13005","volume":"41","author":"WJ Wong","year":"2019","unstructured":"Wong, W. J. & Pozdnyakova, O. Myeloproliferative neoplasms: Diagnostic workup of the cythemic patient. Int. J. Lab. Hematol. 41(Suppl 1), 142\u2013150. https:\/\/doi.org\/10.1111\/ijlh.13005 (2019).","journal-title":"Int. J. Lab. Hematol."},{"key":"82826_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-49942-z","author":"K Kimura","year":"2019","unstructured":"Kimura, K. et al. A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA. Sci. Rep. https:\/\/doi.org\/10.1038\/s41598-019-49942-z (2019).","journal-title":"Sci. Rep."},{"key":"82826_CR9","unstructured":"9Chen, T. & Guestrin, C. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785\u2013794 (ACM)."},{"key":"82826_CR10","doi-asserted-by":"publisher","first-page":"13385","DOI":"10.1038\/s41598-019-49942-z","volume":"9","author":"K Kimura","year":"2019","unstructured":"Kimura, K. et al. A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA. Sci. Rep. 9, 13385. https:\/\/doi.org\/10.1038\/s41598-019-49942-z (2019).","journal-title":"Sci. Rep."},{"key":"82826_CR11","doi-asserted-by":"publisher","first-page":"317","DOI":"10.14670\/HH-20.317","volume":"20","author":"J Thiele","year":"2005","unstructured":"Thiele, J. & Kvasnicka, H. M. Diagnostic impact of bone marrow histopathology in polycythemia vera (PV). Histol. Histopathol. 20, 317\u2013328. https:\/\/doi.org\/10.14670\/HH-20.317 (2005).","journal-title":"Histol. Histopathol."},{"key":"82826_CR12","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1002\/ajh.24837","volume":"92","author":"HM Kvasnicka","year":"2017","unstructured":"Kvasnicka, H. M. et al. European LeukemiaNet study on the reproducibility of bone marrow features in masked polycythemia vera and differentiation from essential thrombocythemia. Am. J. Hematol. 92, 1062\u20131067. https:\/\/doi.org\/10.1002\/ajh.24837 (2017).","journal-title":"Am. J. Hematol."},{"key":"82826_CR13","doi-asserted-by":"publisher","first-page":"4223","DOI":"10.1182\/blood.V130.Suppl_1.4223.4223","volume":"130","author":"M Meggendorfer","year":"2017","unstructured":"Meggendorfer, M., Walter, W., Haferlach, C., Kern, W. & Haferlach, T. Deep learning algorithms support distinction of PV, PMF, and ET based on clinical and genetic markers. Blood 130, 4223\u20134223. https:\/\/doi.org\/10.1182\/blood.V130.Suppl_1.4223.4223 (2017).","journal-title":"Blood"},{"key":"82826_CR14","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1093\/ajcp\/aqw222","volume":"147","author":"J Song","year":"2017","unstructured":"Song, J. et al. Comparison of the mutational profiles of primary myelofibrosis, polycythemia vera, and essential thrombocytosis. Am. J. Clin. Pathol. 147, 444\u2013452. https:\/\/doi.org\/10.1093\/ajcp\/aqw222 (2017).","journal-title":"Am. J. Clin. Pathol."},{"key":"82826_CR15","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/s41598-017-18564-8","volume":"8","author":"G Guncar","year":"2018","unstructured":"Guncar, G. et al. An application of machine learning to haematological diagnosis. Sci. Rep. 8, 411. https:\/\/doi.org\/10.1038\/s41598-017-18564-8 (2018).","journal-title":"Sci. Rep."},{"key":"82826_CR16","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1136\/jclinpath-2012-200930","volume":"65","author":"C Briggs","year":"2012","unstructured":"Briggs, C., Longair, I., Kumar, P., Singh, D. & Machin, S. J. Performance evaluation of the Sysmex haematology XN modular system. J. Clin. Pathol. 65, 1024\u20131030. https:\/\/doi.org\/10.1136\/jclinpath-2012-200930 (2012).","journal-title":"J. Clin. Pathol."},{"key":"82826_CR17","first-page":"1128","volume":"90","author":"J Thiele","year":"2005","unstructured":"Thiele, J. et al. European consensus on grading bone marrow fibrosis and assessment of cellularity. Haematologica 90, 1128\u20131132 (2005).","journal-title":"Haematologica"},{"key":"82826_CR18","unstructured":"18Lundberg, S. M. & Lee, S.-I. In Advances in Neural Information Processing Systems 30 (eds I. Guyon et al.) 4765\u20134774 (Curran Associates, Inc., 2017)."},{"key":"82826_CR19","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1186\/1471-2105-12-77","volume":"12","author":"X Robin","year":"2011","unstructured":"Robin, X. et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77. https:\/\/doi.org\/10.1186\/1471-2105-12-77 (2011).","journal-title":"BMC Bioinform."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-82826-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-82826-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-82826-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T22:13:26Z","timestamp":1670019206000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-82826-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,9]]},"references-count":19,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["82826"],"URL":"https:\/\/doi.org\/10.1038\/s41598-021-82826-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,9]]},"assertion":[{"value":"22 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The Department of Next Generation of Hematology Laboratory Medicine at Juntendo University Graduate School of Medicine\u00a0has been endowed by Sysmex (Kobe, Japan) to develop and validate new diagnostic technology and to conduct academic research in hematology through collaborations. KK, IT, and KU are employees of Sysmex. The study was performed by scientifically proper methods without any bias. This work was supported in part by Grants-in Aid for Scientific Research (18K07424 to YT), International Joint Research Programs (19KK0221 to YT) from Japan Society for the Promotion of Science.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"3367"}}