Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors

Abstract To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013–2019 were reviewed and categorized into low-grade (very lo...

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Autores principales:	Hairui Chu, Peipei Pang, Jian He, Desheng Zhang, Mei Zhang, Yingying Qiu, Xiaofen Li, Pinggui Lei, Bing Fan, Rongchun Xu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e1f3c7bc5bbd4db0872102f0344828c8
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spelling	oai:doaj.org-article:e1f3c7bc5bbd4db0872102f0344828c82021-12-02T17:52:40ZValue of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors10.1038/s41598-021-91508-52045-2322https://doaj.org/article/e1f3c7bc5bbd4db0872102f0344828c82021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91508-5https://doaj.org/toc/2045-2322Abstract To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013–2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient’s enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images. The proposed radiomics model exhibited effective risk-grade prediction of GIST. For the training group, the area under curve (AUC), sensitivity, specificity, and accuracy rate were 0.793 (95%CI: 0.733–0.854), 83.3%, 64.3%, and 72.7%, respectively; the corresponding values for the test group were 0.791 (95%CI: 0.696–0.886), 84.2%, 69.3%, and 75.9%, respectively. There were significant differences in age (t value: − 3.133, P = 0.008), maximum tumor diameter (Z value: − 12.163, P = 0.000) and tumor morphology (χ2 value:10.409, P = 0.001) between the two groups, which were used to establish a clinical prediction model. The area under the receiver operating characteristic curve of the clinical model was 0.718 (95%CI: 0.659–0.776). The proposed CT-enhanced radiomics model exhibited better accuracy and effective performance than the clinical model, which can be used for the assessment of risk grades of GIST.Hairui ChuPeipei PangJian HeDesheng ZhangMei ZhangYingying QiuXiaofen LiPinggui LeiBing FanRongchun XuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hairui Chu Peipei Pang Jian He Desheng Zhang Mei Zhang Yingying Qiu Xiaofen Li Pinggui Lei Bing Fan Rongchun Xu Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors
description	Abstract To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013–2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient’s enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images. The proposed radiomics model exhibited effective risk-grade prediction of GIST. For the training group, the area under curve (AUC), sensitivity, specificity, and accuracy rate were 0.793 (95%CI: 0.733–0.854), 83.3%, 64.3%, and 72.7%, respectively; the corresponding values for the test group were 0.791 (95%CI: 0.696–0.886), 84.2%, 69.3%, and 75.9%, respectively. There were significant differences in age (t value: − 3.133, P = 0.008), maximum tumor diameter (Z value: − 12.163, P = 0.000) and tumor morphology (χ2 value:10.409, P = 0.001) between the two groups, which were used to establish a clinical prediction model. The area under the receiver operating characteristic curve of the clinical model was 0.718 (95%CI: 0.659–0.776). The proposed CT-enhanced radiomics model exhibited better accuracy and effective performance than the clinical model, which can be used for the assessment of risk grades of GIST.
format	article
author	Hairui Chu Peipei Pang Jian He Desheng Zhang Mei Zhang Yingying Qiu Xiaofen Li Pinggui Lei Bing Fan Rongchun Xu
author_facet	Hairui Chu Peipei Pang Jian He Desheng Zhang Mei Zhang Yingying Qiu Xiaofen Li Pinggui Lei Bing Fan Rongchun Xu
author_sort	Hairui Chu
title	Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors
title_short	Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors
title_full	Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors
title_fullStr	Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors
title_full_unstemmed	Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors
title_sort	value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e1f3c7bc5bbd4db0872102f0344828c8
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Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors

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