Dual-energy CT texture analysis with machine learning for the evaluation and characterization of cervical lymphadenopathy

  • 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 normal lymph nodes. Materials and methods: A retrospective evaluation of 412 cervical nodes from 5 different patient groups (50 patients in total) having undergone DECT of the neck between 2013 and 2015 was performed: (1) HNSCC with pathology proven metastatic adenopathy, (2) HNSCC with pathology proven benign nodes (controls for (1)), (3) lymphoma, (4) inflammatory, and (5) normal nodes (controls for (3) and (4)). Texture analysis was performed with TexRAD® software using two independent sets of contours to assess the impact of inter-rater variation. Two machine learning algorithms (Random Forests (RF) and Gradient Boosting Machine (GBM)) were used with independent training and testing sets and determination of accuracy, sensitivity, specificity, PPV, NPV, and AUC. Results: In the independent testing (prediction) sets, the accuracy for distinguishing different groups of pathologic nodes or normal nodes ranged between 80 and 95%. The models generated using texture data extracted from the independent contour sets had substantial to almost perfect agreement. The accuracy, sensitivity, specificity, PPV, and NPV for correctly classifying a lymph node as malignant (i.e. metastatic HNSCC or lymphoma) versus benign were 92%, 91%, 93%, 95%, 87%, respectively. Conclusion: Machine learning assisted-DECT texture analysis can help distinguish different nodal pathology and normal nodes with a high accuracy.

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Author:Matthew Seidler, Behzad Forghani, Caroline Reinhold, Almudena Pérez-Lara, Griselda Romero-Sanchez, Nikesh Muthukrishnan, Julian WichmannORCiDGND, Gabriel Melki, Eugene Yu, Reza Forghani
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/31406557
Parent Title (English):Computational and structural biotechnology journal
Publisher:Research Network of Computational and Structural Biotechnology (RNCSB)
Place of publication:Gotenburg
Document Type:Article
Year of Completion:2019
Date of first Publication:2019/07/16
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2019/08/29
Tag:Artificial intelligence; Dual energy CT; Lymph nodes; Machine learning; Radiomics; Texture analysis
Page Number:7
First Page:1009
Last Page:1015
© 2019 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell - Keine Bearbeitung 4.0