Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images

Abstract Background The objective of this study is to explore the value of two-dimensional (2D) and three-dimensional (3D) radiomics models based on enhanced computed tomography (CT) images in predicting BRCA gene mutations in patients with epithelial ovarian cancer. Methods The clinical and imaging...

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Autores principales:	Liu Mingzhu, Ge Yaqiong, Li Mengru, Wei Wei
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Ovarian cancer BRCA gene Radiomics Mutation Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/65ab5d89b7aa4a68a005ee2a4e6b32cd
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spelling	oai:doaj.org-article:65ab5d89b7aa4a68a005ee2a4e6b32cd2021-11-28T12:30:26ZPrediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images10.1186/s12880-021-00711-31471-2342https://doaj.org/article/65ab5d89b7aa4a68a005ee2a4e6b32cd2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12880-021-00711-3https://doaj.org/toc/1471-2342Abstract Background The objective of this study is to explore the value of two-dimensional (2D) and three-dimensional (3D) radiomics models based on enhanced computed tomography (CT) images in predicting BRCA gene mutations in patients with epithelial ovarian cancer. Methods The clinical and imaging data of 106 patients with ovarian cancer confirmed by surgery and pathology were retrospectively analyzed and genetic testing was performed. Radiomics features extracted from the 2D and 3D regions of interest of the patients’ primary tumor lesions were selected in the training set using the maximum correlation and minimum redundancy method. Then, the best features were selected through Lasso tenfold cross-validation. Feature subsets were employed to establish a radiomics model. The model’s performance was evaluated via area under the receiver operating characteristic curve analysis and its clinical validity was assessed by using the model’s decision curve. Results On the validation set, the area under the curve values of the 2D, 3D, and 2D + 3D combined models were 0.78 (0.61–0.96), 0.75 (0.55–0.92), and 0.82 (0.61–0.96), respectively. However, the DeLong test P values between the three pairs of models were all > 0.05. The decision curve analysis showed that the radiomics model had a high net benefit across all high-risk threshold probabilities. Conclusions The three radiomics models can predict the BRCA gene mutation in ovarian cancer, and there were no statistically significant differences between the prediction performance of the three models.Liu MingzhuGe YaqiongLi MengruWei WeiBMCarticleOvarian cancerBRCA geneRadiomicsMutationMedical technologyR855-855.5ENBMC Medical Imaging, Vol 21, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ovarian cancer BRCA gene Radiomics Mutation Medical technology R855-855.5
spellingShingle	Ovarian cancer BRCA gene Radiomics Mutation Medical technology R855-855.5 Liu Mingzhu Ge Yaqiong Li Mengru Wei Wei Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images
description	Abstract Background The objective of this study is to explore the value of two-dimensional (2D) and three-dimensional (3D) radiomics models based on enhanced computed tomography (CT) images in predicting BRCA gene mutations in patients with epithelial ovarian cancer. Methods The clinical and imaging data of 106 patients with ovarian cancer confirmed by surgery and pathology were retrospectively analyzed and genetic testing was performed. Radiomics features extracted from the 2D and 3D regions of interest of the patients’ primary tumor lesions were selected in the training set using the maximum correlation and minimum redundancy method. Then, the best features were selected through Lasso tenfold cross-validation. Feature subsets were employed to establish a radiomics model. The model’s performance was evaluated via area under the receiver operating characteristic curve analysis and its clinical validity was assessed by using the model’s decision curve. Results On the validation set, the area under the curve values of the 2D, 3D, and 2D + 3D combined models were 0.78 (0.61–0.96), 0.75 (0.55–0.92), and 0.82 (0.61–0.96), respectively. However, the DeLong test P values between the three pairs of models were all > 0.05. The decision curve analysis showed that the radiomics model had a high net benefit across all high-risk threshold probabilities. Conclusions The three radiomics models can predict the BRCA gene mutation in ovarian cancer, and there were no statistically significant differences between the prediction performance of the three models.
format	article
author	Liu Mingzhu Ge Yaqiong Li Mengru Wei Wei
author_facet	Liu Mingzhu Ge Yaqiong Li Mengru Wei Wei
author_sort	Liu Mingzhu
title	Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images
title_short	Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images
title_full	Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images
title_fullStr	Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images
title_full_unstemmed	Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images
title_sort	prediction of brca gene mutation status in epithelial ovarian cancer by radiomics models based on 2d and 3d ct images
publisher	BMC
publishDate	2021
url	https://doaj.org/article/65ab5d89b7aa4a68a005ee2a4e6b32cd
work_keys_str_mv	AT liumingzhu predictionofbrcagenemutationstatusinepithelialovariancancerbyradiomicsmodelsbasedon2dand3dctimages AT geyaqiong predictionofbrcagenemutationstatusinepithelialovariancancerbyradiomicsmodelsbasedon2dand3dctimages AT limengru predictionofbrcagenemutationstatusinepithelialovariancancerbyradiomicsmodelsbasedon2dand3dctimages AT weiwei predictionofbrcagenemutationstatusinepithelialovariancancerbyradiomicsmodelsbasedon2dand3dctimages
_version_	1718407959102357504

Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images

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