Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features

Abstract Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML), classified by a translocation between chromosomes 15 and 17 [t(15;17)], that is considered a true oncologic emergency though appropriate therapy is considered curative. Therapy is often initiated on clinical su...

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Autores principales:	John-William Sidhom, Ingharan J. Siddarthan, Bo-Shiun Lai, Adam Luo, Bryan C. Hambley, Jennifer Bynum, Amy S. Duffield, Michael B. Streiff, Alison R. Moliterno, Philip Imus, Christian B. Gocke, Lukasz P. Gondek, Amy E. DeZern, Alexander S. Baras, Thomas Kickler, Mark J. Levis, Eugene Shenderov
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/dd046aeb33da4cd3974afb7aa84c2bbd
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spelling	oai:doaj.org-article:dd046aeb33da4cd3974afb7aa84c2bbd2021-12-02T17:15:35ZDeep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features10.1038/s41698-021-00179-y2397-768Xhttps://doaj.org/article/dd046aeb33da4cd3974afb7aa84c2bbd2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41698-021-00179-yhttps://doaj.org/toc/2397-768XAbstract Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML), classified by a translocation between chromosomes 15 and 17 [t(15;17)], that is considered a true oncologic emergency though appropriate therapy is considered curative. Therapy is often initiated on clinical suspicion, informed by both clinical presentation as well as direct visualization of the peripheral smear. We hypothesized that genomic imprinting of morphologic features learned by deep learning pattern recognition would have greater discriminatory power and consistency compared to humans, thereby facilitating identification of t(15;17) positive APL. By applying both cell-level and patient-level classification linked to t(15;17) PML/RARA ground-truth, we demonstrate that deep learning is capable of distinguishing APL in both discovery and prospective independent cohort of patients. Furthermore, we extract learned information from the trained network to identify previously undescribed morphological features of APL. The deep learning method we describe herein potentially allows a rapid, explainable, and accurate physician-aid for diagnosing APL at the time of presentation in any resource-poor or -rich medical setting given the universally available peripheral smear.John-William SidhomIngharan J. SiddarthanBo-Shiun LaiAdam LuoBryan C. HambleyJennifer BynumAmy S. DuffieldMichael B. StreiffAlison R. MoliternoPhilip ImusChristian B. GockeLukasz P. GondekAmy E. DeZernAlexander S. BarasThomas KicklerMark J. LevisEugene ShenderovNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Precision Oncology, Vol 5, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 John-William Sidhom Ingharan J. Siddarthan Bo-Shiun Lai Adam Luo Bryan C. Hambley Jennifer Bynum Amy S. Duffield Michael B. Streiff Alison R. Moliterno Philip Imus Christian B. Gocke Lukasz P. Gondek Amy E. DeZern Alexander S. Baras Thomas Kickler Mark J. Levis Eugene Shenderov Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features
description	Abstract Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML), classified by a translocation between chromosomes 15 and 17 [t(15;17)], that is considered a true oncologic emergency though appropriate therapy is considered curative. Therapy is often initiated on clinical suspicion, informed by both clinical presentation as well as direct visualization of the peripheral smear. We hypothesized that genomic imprinting of morphologic features learned by deep learning pattern recognition would have greater discriminatory power and consistency compared to humans, thereby facilitating identification of t(15;17) positive APL. By applying both cell-level and patient-level classification linked to t(15;17) PML/RARA ground-truth, we demonstrate that deep learning is capable of distinguishing APL in both discovery and prospective independent cohort of patients. Furthermore, we extract learned information from the trained network to identify previously undescribed morphological features of APL. The deep learning method we describe herein potentially allows a rapid, explainable, and accurate physician-aid for diagnosing APL at the time of presentation in any resource-poor or -rich medical setting given the universally available peripheral smear.
format	article
author	John-William Sidhom Ingharan J. Siddarthan Bo-Shiun Lai Adam Luo Bryan C. Hambley Jennifer Bynum Amy S. Duffield Michael B. Streiff Alison R. Moliterno Philip Imus Christian B. Gocke Lukasz P. Gondek Amy E. DeZern Alexander S. Baras Thomas Kickler Mark J. Levis Eugene Shenderov
author_facet	John-William Sidhom Ingharan J. Siddarthan Bo-Shiun Lai Adam Luo Bryan C. Hambley Jennifer Bynum Amy S. Duffield Michael B. Streiff Alison R. Moliterno Philip Imus Christian B. Gocke Lukasz P. Gondek Amy E. DeZern Alexander S. Baras Thomas Kickler Mark J. Levis Eugene Shenderov
author_sort	John-William Sidhom
title	Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features
title_short	Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features
title_full	Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features
title_fullStr	Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features
title_full_unstemmed	Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features
title_sort	deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dd046aeb33da4cd3974afb7aa84c2bbd
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Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features

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