A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography

Abstract This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian...

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Autores principales:	Shiyun Li, Jiaqi Liu, Yuanhuan Xiong, Peipei Pang, Pinggui Lei, Huachun Zou, Mei Zhang, Bing Fan, Puying Luo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/501f5cec590c40679091427e637db69a
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spelling	oai:doaj.org-article:501f5cec590c40679091427e637db69a2021-12-02T17:32:59ZA radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography10.1038/s41598-021-87775-x2045-2322https://doaj.org/article/501f5cec590c40679091427e637db69a2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87775-xhttps://doaj.org/toc/2045-2322Abstract This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.Shiyun LiJiaqi LiuYuanhuan XiongPeipei PangPinggui LeiHuachun ZouMei ZhangBing FanPuying LuoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shiyun Li Jiaqi Liu Yuanhuan Xiong Peipei Pang Pinggui Lei Huachun Zou Mei Zhang Bing Fan Puying Luo A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography
description	Abstract This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.
format	article
author	Shiyun Li Jiaqi Liu Yuanhuan Xiong Peipei Pang Pinggui Lei Huachun Zou Mei Zhang Bing Fan Puying Luo
author_facet	Shiyun Li Jiaqi Liu Yuanhuan Xiong Peipei Pang Pinggui Lei Huachun Zou Mei Zhang Bing Fan Puying Luo
author_sort	Shiyun Li
title	A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography
title_short	A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography
title_full	A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography
title_fullStr	A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography
title_full_unstemmed	A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography
title_sort	radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/501f5cec590c40679091427e637db69a
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A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography

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