Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen

Abstract 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...

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Autores principales:	Konobu Kimura, Tomohiko Ai, Yuki Horiuchi, Akihiko Matsuzaki, Kumiko Nishibe, Setsuko Marutani, Kaori Saito, Kimiko Kaniyu, Ikki Takehara, Kinya Uchihashi, Akimichi Ohsaka, Yoko Tabe
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e9d17be6387a4566986f527f32e675a1
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spelling	oai:doaj.org-article:e9d17be6387a4566986f527f32e675a12021-12-02T12:09:05ZAutomated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen10.1038/s41598-021-82826-92045-2322https://doaj.org/article/e9d17be6387a4566986f527f32e675a12021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82826-9https://doaj.org/toc/2045-2322Abstract 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: > 90% sensitivity and > 90% 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.Konobu KimuraTomohiko AiYuki HoriuchiAkihiko MatsuzakiKumiko NishibeSetsuko MarutaniKaori SaitoKimiko KaniyuIkki TakeharaKinya UchihashiAkimichi OhsakaYoko TabeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Konobu Kimura Tomohiko Ai Yuki Horiuchi Akihiko Matsuzaki Kumiko Nishibe Setsuko Marutani Kaori Saito Kimiko Kaniyu Ikki Takehara Kinya Uchihashi Akimichi Ohsaka Yoko Tabe Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen
description	Abstract 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: > 90% sensitivity and > 90% 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.
format	article
author	Konobu Kimura Tomohiko Ai Yuki Horiuchi Akihiko Matsuzaki Kumiko Nishibe Setsuko Marutani Kaori Saito Kimiko Kaniyu Ikki Takehara Kinya Uchihashi Akimichi Ohsaka Yoko Tabe
author_facet	Konobu Kimura Tomohiko Ai Yuki Horiuchi Akihiko Matsuzaki Kumiko Nishibe Setsuko Marutani Kaori Saito Kimiko Kaniyu Ikki Takehara Kinya Uchihashi Akimichi Ohsaka Yoko Tabe
author_sort	Konobu Kimura
title	Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen
title_short	Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen
title_full	Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen
title_fullStr	Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen
title_full_unstemmed	Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen
title_sort	automated diagnostic support system with deep learning algorithms for distinction of philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e9d17be6387a4566986f527f32e675a1
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Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen

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