Výsledky vyhledávání - "Daniel A Orringer"

Akademický článek

Automated histologic diagnosis of CNS tumors with machine learning

Autor: Siri Sahib S Khalsa, Todd C Hollon, Arjun Adapa, Esteban Urias, Sudharsan Srinivasan, Neil Jairath, Julianne Szczepanski, Peter Ouillette, Sandra Camelo-Piragua, Daniel A Orringer

Publikováno v: CNS Oncology, Vol 9, Iss 2 (2020)

The discovery of a new mass involving the brain or spine typically prompts referral to a neurosurgeon to consider biopsy or surgical resection. Intraoperative decision-making depends significantly on the histologic diagnosis, which is often establish

Externí odkaz: https://doaj.org/article/23227fb891224a6c8bc49a555ee23655

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Akademický článek

Synthetic high-density lipoprotein nanoparticles for the treatment of Niemann–Pick diseases

Autor: Mark L. Schultz, Maria V. Fawaz, Ruth D. Azaria, Todd C. Hollon, Elaine A. Liu, Thaddeus J. Kunkel, Troy A. Halseth, Kelsey L. Krus, Ran Ming, Emily E. Morin, Hayley S. McLoughlin, David D. Bushart, Henry L. Paulson, Vikram G. Shakkottai, Daniel A. Orringer, Anna S. Schwendeman, Andrew P. Lieberman

Publikováno v: BMC Medicine, Vol 17, Iss 1, Pp 1-18 (2019)

Abstract Background Niemann–Pick disease type C is a fatal and progressive neurodegenerative disorder characterized by the accumulation of unesterified cholesterol in late endosomes and lysosomes. We sought to develop new therapeutics for this diso

Externí odkaz: https://doaj.org/article/e83baffb0e3847bbafc88e3c4bd749f6

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Akademický článek

An automated tissue-to-diagnosis pipeline using intraoperative stimulated Raman histology and deep learning

Autor: Todd C. Hollon, Daniel A. Orringer

Publikováno v: Molecular & Cellular Oncology, Vol 7, Iss 3 (2020)

We recently developed and validated a bedside tissue-to-diagnosis pipeline using stimulated Raman histology (SRH), a label-free optical imaging method, and deep convolutional neural networks (CNN) in prospective clinical trial. Our CNN learned a hier

Externí odkaz: https://doaj.org/article/0d6c10081cd34199a9f07ddf68757fa2

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Supplementary Figure 2 from Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology

Autor: Daniel A. Orringer, Sandra Camelo-Piragua, Karin Muraszko, Andrew P. Lieberman, Matija Snuderl, Kathryn McFadden, Cormac O. Maher, Hugh Garton, Mia R. Garrard, Yashar S. Niknafs, Balaji Pandian, Spencer Lewis, Todd C. Hollon

Random forest model 2. A) Example decision tree from random forest model 2. B) Out-of-bag, low-grade, and high-grade tumor classification error across 500 grown decision trees. C) Image feature importance for model 2. Similar to model 1, nuclei densi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::512d76e580a5a08f7d2617c78e0b2805
https://doi.org/10.1158/0008-5472.22417835

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Data from Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology

Accurate histopathologic diagnosis is essential for providing optimal surgical management of pediatric brain tumors. Current methods for intraoperative histology are time- and labor-intensive and often introduce artifact that limit interpretation. St

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88d67647606d98bed215bf462044e10d
https://doi.org/10.1158/0008-5472.c.6509957

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