Dual-Energy CT Texture Analysis With Machine Learning for the Evaluation and Characterization of Cervical Lymphadenopathy

Dual-Energy CT Texture Analysis With Machine Learning for the Evaluation and Characterization of Cervical Lymphadenopathy

ABSTRACT: Purpose: To determine whether machine learning assisted-texture analysis of multi-energy virtual monochromatic image (VMI) datasets from dual-energy CT (DECT) can be used to differentiate metastatic head and neck squamous cell carcinoma (HNSCC) lymph nodes from lymphoma, inflammatory, or...

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Bibliographic Details
Main Authors:	Matthew Seidler, Behzad Forghani, Caroline Reinhold, Almudena Pérez-Lara, Griselda Romero-Sanchez, Nikesh Muthukrishnan, Julian L. Wichmann, Gabriel Melki, Eugene Yu, Reza Forghani
Format:	Article
Language:	English
Published:	Elsevier 2019-01-01
Series:	Computational and Structural Biotechnology Journal
Online Access:	http://www.sciencedirect.com/science/article/pii/S200103701830309X

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