Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images

Abstract Background Tongue squamous cell carcinoma (TSCC) is one of the most difficult malignancies to control. It displays particular and aggressive behaviour even at an early stage. The purpose of this paper is to explore the value of radiomics based on magnetic resonance fat-suppressed T2-weighte...

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Autores principales:	Baoting Yu, Chencui Huang, Jingxu Xu, Shuo Liu, Yuyao Guan, Tong Li, Xuewei Zheng, Jun Ding
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Tongue squamous cell carcinoma (TSCC) Radiomics Texture analysis Degree of pathological differentiation Magnetic resonance imaging (MRI) Dentistry RK1-715
Acceso en línea:	https://doaj.org/article/b62f25b5ec69487aaa6e5cc14f5afaa8
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spelling	oai:doaj.org-article:b62f25b5ec69487aaa6e5cc14f5afaa82021-11-21T12:32:20ZPrediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images10.1186/s12903-021-01947-91472-6831https://doaj.org/article/b62f25b5ec69487aaa6e5cc14f5afaa82021-11-01T00:00:00Zhttps://doi.org/10.1186/s12903-021-01947-9https://doaj.org/toc/1472-6831Abstract Background Tongue squamous cell carcinoma (TSCC) is one of the most difficult malignancies to control. It displays particular and aggressive behaviour even at an early stage. The purpose of this paper is to explore the value of radiomics based on magnetic resonance fat-suppressed T2-weighted images in predicting the degree of pathological differentiation of TSCC. Methods Retrospective analysis of 127 patients with TSCC who were randomly divided into a primary cohort and a test cohort, including well-differentiated, moderately differentiated and poorly differentiated. The tumour regions were manually labelled in fat-suppressed T2-weighted imaging (FS-T2WI), and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method. The model was established by the logistic regression classifier using a 5-fold cross-validation method, applied to all data and evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results In total, 1132 features were extracted, and seven features were selected for modelling. The AUC in the logistic regression model for well-differentiated TSCC was 0.90 with specificity and precision values of 0.92 and 0.78, respectively, and the sensitivity for poorly differentiated TSCC was 0.74. Conclusions The MRI-based radiomics signature could discriminate between well-differentiated, moderately differentiated and poorly differentiated TSCC and might be used as a biomarker for preoperative grading.Baoting YuChencui HuangJingxu XuShuo LiuYuyao GuanTong LiXuewei ZhengJun DingBMCarticleTongue squamous cell carcinoma (TSCC)RadiomicsTexture analysisDegree of pathological differentiationMagnetic resonance imaging (MRI)DentistryRK1-715ENBMC Oral Health, Vol 21, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Tongue squamous cell carcinoma (TSCC) Radiomics Texture analysis Degree of pathological differentiation Magnetic resonance imaging (MRI) Dentistry RK1-715
spellingShingle	Tongue squamous cell carcinoma (TSCC) Radiomics Texture analysis Degree of pathological differentiation Magnetic resonance imaging (MRI) Dentistry RK1-715 Baoting Yu Chencui Huang Jingxu Xu Shuo Liu Yuyao Guan Tong Li Xuewei Zheng Jun Ding Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images
description	Abstract Background Tongue squamous cell carcinoma (TSCC) is one of the most difficult malignancies to control. It displays particular and aggressive behaviour even at an early stage. The purpose of this paper is to explore the value of radiomics based on magnetic resonance fat-suppressed T2-weighted images in predicting the degree of pathological differentiation of TSCC. Methods Retrospective analysis of 127 patients with TSCC who were randomly divided into a primary cohort and a test cohort, including well-differentiated, moderately differentiated and poorly differentiated. The tumour regions were manually labelled in fat-suppressed T2-weighted imaging (FS-T2WI), and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method. The model was established by the logistic regression classifier using a 5-fold cross-validation method, applied to all data and evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results In total, 1132 features were extracted, and seven features were selected for modelling. The AUC in the logistic regression model for well-differentiated TSCC was 0.90 with specificity and precision values of 0.92 and 0.78, respectively, and the sensitivity for poorly differentiated TSCC was 0.74. Conclusions The MRI-based radiomics signature could discriminate between well-differentiated, moderately differentiated and poorly differentiated TSCC and might be used as a biomarker for preoperative grading.
format	article
author	Baoting Yu Chencui Huang Jingxu Xu Shuo Liu Yuyao Guan Tong Li Xuewei Zheng Jun Ding
author_facet	Baoting Yu Chencui Huang Jingxu Xu Shuo Liu Yuyao Guan Tong Li Xuewei Zheng Jun Ding
author_sort	Baoting Yu
title	Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images
title_short	Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images
title_full	Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images
title_fullStr	Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images
title_full_unstemmed	Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images
title_sort	prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images
publisher	BMC
publishDate	2021
url	https://doaj.org/article/b62f25b5ec69487aaa6e5cc14f5afaa8
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Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images

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