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

Yonsei Medical Journal 2020년 61권 10호 p.895 ~ 900
배소희, 최윤성, 손범석, 안성수, 이승구, 양재문, 김진나,
소속 상세정보
배소희 ( Bae So-Hi ) - National Health Insurance Service Ilsan Hospital Department of Radiology
최윤성 ( Choi Yoon-Seong ) - Yonsei University College of Medicine Department of Radiology
손범석 ( Sohn Beom-Seok ) - Yonsei University College of Medicine Department of Radiology
안성수 ( Ahn Sung-Soo ) - Yonsei University College of Medicine Department of Radiology
이승구 ( Lee Seung-Koo ) - Yonsei University College of Medicine Department of Radiology
양재문 ( Yang Jae-Moon ) - Yonsei University College of Medicine Department of Radiology
김진나 ( Kim Jin-Na ) - Yonsei University College of Medicine Department of Radiology

Abstract


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.

키워드

Radiomics; oropharynx; squamous cell carcinoma; lymphoma; magnetic resonance imaging

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