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Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach

The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) w...

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Autores principales: Bae, Sohi, Choi, Yoon Seong, Sohn, Beomseok, Ahn, Sung Soo, Lee, Seung-Koo, Yang, Jaemoon, Kim, Jinna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Yonsei University College of Medicine 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515782/
https://www.ncbi.nlm.nih.gov/pubmed/32975065
http://dx.doi.org/10.3349/ymj.2020.61.10.895
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author Bae, Sohi
Choi, Yoon Seong
Sohn, Beomseok
Ahn, Sung Soo
Lee, Seung-Koo
Yang, Jaemoon
Kim, Jinna
author_facet Bae, Sohi
Choi, Yoon Seong
Sohn, Beomseok
Ahn, Sung Soo
Lee, Seung-Koo
Yang, Jaemoon
Kim, Jinna
author_sort Bae, Sohi
collection PubMed
description The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) were reviewed retrospectively. Tumors were semi-automatically segmented on contrast-enhanced T1-weighted images registered to T2-weighted images, and radiomic features (n=202) were extracted from contrast-enhanced T1- and T2-weighted images. The radiomics classifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics classifier and visual assessment by two head and neck radiologists were evaluated using receiver operating characteristic (ROC) analyses for distinguishing SCC from lymphoma. Nineteen radiomics features were selected at least twice during the five-fold cross-validation. The mean area under the ROC curve (AUC) of the radiomics classifier was 0.750 [95% confidence interval (CI), 0.613–0.887], with a sensitivity of 84.2%, specificity of 60.3%, and an accuracy of 65.5%. Two human readers yielded AUCs of 0.613 (95% CI, 0.467–0.759) and 0.663 (95% CI, 0.531–0.795), respectively. The radiomics-based machine learning model can be useful for differentiating SCC from lymphoma of the oropharynx.
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spelling pubmed-75157822020-10-02 Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach Bae, Sohi Choi, Yoon Seong Sohn, Beomseok Ahn, Sung Soo Lee, Seung-Koo Yang, Jaemoon Kim, Jinna Yonsei Med J Brief Communication The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) were reviewed retrospectively. Tumors were semi-automatically segmented on contrast-enhanced T1-weighted images registered to T2-weighted images, and radiomic features (n=202) were extracted from contrast-enhanced T1- and T2-weighted images. The radiomics classifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics classifier and visual assessment by two head and neck radiologists were evaluated using receiver operating characteristic (ROC) analyses for distinguishing SCC from lymphoma. Nineteen radiomics features were selected at least twice during the five-fold cross-validation. The mean area under the ROC curve (AUC) of the radiomics classifier was 0.750 [95% confidence interval (CI), 0.613–0.887], with a sensitivity of 84.2%, specificity of 60.3%, and an accuracy of 65.5%. Two human readers yielded AUCs of 0.613 (95% CI, 0.467–0.759) and 0.663 (95% CI, 0.531–0.795), respectively. The radiomics-based machine learning model can be useful for differentiating SCC from lymphoma of the oropharynx. Yonsei University College of Medicine 2020-10-01 2020-09-22 /pmc/articles/PMC7515782/ /pubmed/32975065 http://dx.doi.org/10.3349/ymj.2020.61.10.895 Text en © Copyright: Yonsei University College of Medicine 2020 https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Brief Communication
Bae, Sohi
Choi, Yoon Seong
Sohn, Beomseok
Ahn, Sung Soo
Lee, Seung-Koo
Yang, Jaemoon
Kim, Jinna
Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach
title Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach
title_full Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach
title_fullStr Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach
title_full_unstemmed Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach
title_short Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach
title_sort squamous cell carcinoma and lymphoma of the oropharynx: differentiation using a radiomics approach
topic Brief Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515782/
https://www.ncbi.nlm.nih.gov/pubmed/32975065
http://dx.doi.org/10.3349/ymj.2020.61.10.895
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