26 36

Cited 0 times in

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

 Sohi Bae  ;  Yoon Seong Choi  ;  Beomseok Sohn  ;  Sung Soo Ahn  ;  Seung Koo Lee  ;  Jaemoon Yang  ;  Jinna Kim 
 YONSEI MEDICAL JOURNAL, Vol.61(10) : 895-900, 2020-10 
Journal Title
Issue Date
Algorithms ; Biopsy ; Carcinoma, Squamous Cell / diagnostic imaging* ; Carcinoma, Squamous Cell / pathology ; Female ; Humans ; Image Enhancement / methods ; Lymphoma / diagnostic imaging* ; Lymphoma / pathology ; Machine Learning ; Magnetic Resonance Imaging / methods* ; Oropharyngeal Neoplasms / diagnostic imaging* ; Oropharyngeal Neoplasms / pathology ; Oropharynx / diagnostic imaging* ; Oropharynx / pathology ; ROC Curve ; Retrospective Studies ; Sensitivity and Specificity ; Treatment Outcome
Radiomics ; lymphoma ; magnetic resonance imaging ; oropharynx ; squamous cell carcinoma
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.
Files in This Item:
T202004073.pdf Download
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jinna(김진아) ORCID logo https://orcid.org/0000-0002-9978-4356
Sohn, Beomseok(손범석) ORCID logo https://orcid.org/0000-0002-6765-8056
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
Yang, Jae Moon(양재문) ORCID logo https://orcid.org/0000-0001-7365-0395
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
Choi, Yoon Seong(최윤성)
사서에게 알리기


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.