Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI

The purpose of this study was to explore whether the Nomogram, which was constructed by combining the Deep learning and Radiomic features of T2-weighted MR images with Clinical factors (NDRC), could accurately predict placenta invasion. This retrospective study included 72 pregnant women with pathol...

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Autores principales:	Qian ShaoYutao Wang, Rongrong Xuan, Yutao Wang, Jian Xu, Menglin Ouyang, Caoqian Yin, Wei Jin
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	radiomics deep learning nomogram magnetic resonance imaging placenta invasion Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/98c2693fab7e4ddfbe8258cdec4b176d
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id	oai:doaj.org-article:98c2693fab7e4ddfbe8258cdec4b176d
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spelling	oai:doaj.org-article:98c2693fab7e4ddfbe8258cdec4b176d2021-11-11T01:12:25ZDeep learning and radiomics analysis for prediction of placenta invasion based on T2WI10.3934/mbe.20213101551-0018https://doaj.org/article/98c2693fab7e4ddfbe8258cdec4b176d2021-07-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021310?viewType=HTMLhttps://doaj.org/toc/1551-0018The purpose of this study was to explore whether the Nomogram, which was constructed by combining the Deep learning and Radiomic features of T2-weighted MR images with Clinical factors (NDRC), could accurately predict placenta invasion. This retrospective study included 72 pregnant women with pathologically confirmed placenta invasion and 40 pregnant women with normal placenta. After 24 gestational weeks, all participants underwent magnetic resonance imaging. The uterus and placenta regions were segmented in magnetic resonance images on sagittal T2WI. Ninety-three radiomics features were extracted from the placenta region, and 128 deep features were extracted from the uterus region using a deep neural network. The least absolute shrinkage and selection operator (LASSO) algorithm was used to filter these 221 features and to form the combined signature. Then the combined signature (CS) and clinical factors were combined to construct a nomogram. The accuracy, sensitivity, specificity and AUC of the nomogram were compared with four machine learning methods. The model NDRC was trained on the dataset of 78 pregnant women in the training cohort. Finally, the model NDRC was compared with four machine learning methods on the independent validation cohort of 34 pregnant women. The results showed that the prediction accuracy, sensitivity, specificity and AUC of the NDRC model were 0.941, 0.952, 0.923 and 0.985 respectively, which outperforms the traditional machine learning methods which rely on radiomics features and deep learning features alone.Qian ShaoYutao WangRongrong XuanYutao WangJian XuMenglin OuyangCaoqian YinWei JinAIMS Pressarticleradiomicsdeep learningnomogrammagnetic resonance imagingplacenta invasionBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6198-6215 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	radiomics deep learning nomogram magnetic resonance imaging placenta invasion Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	radiomics deep learning nomogram magnetic resonance imaging placenta invasion Biotechnology TP248.13-248.65 Mathematics QA1-939 Qian ShaoYutao Wang Rongrong Xuan Yutao Wang Jian Xu Menglin Ouyang Caoqian Yin Wei Jin Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI
description	The purpose of this study was to explore whether the Nomogram, which was constructed by combining the Deep learning and Radiomic features of T2-weighted MR images with Clinical factors (NDRC), could accurately predict placenta invasion. This retrospective study included 72 pregnant women with pathologically confirmed placenta invasion and 40 pregnant women with normal placenta. After 24 gestational weeks, all participants underwent magnetic resonance imaging. The uterus and placenta regions were segmented in magnetic resonance images on sagittal T2WI. Ninety-three radiomics features were extracted from the placenta region, and 128 deep features were extracted from the uterus region using a deep neural network. The least absolute shrinkage and selection operator (LASSO) algorithm was used to filter these 221 features and to form the combined signature. Then the combined signature (CS) and clinical factors were combined to construct a nomogram. The accuracy, sensitivity, specificity and AUC of the nomogram were compared with four machine learning methods. The model NDRC was trained on the dataset of 78 pregnant women in the training cohort. Finally, the model NDRC was compared with four machine learning methods on the independent validation cohort of 34 pregnant women. The results showed that the prediction accuracy, sensitivity, specificity and AUC of the NDRC model were 0.941, 0.952, 0.923 and 0.985 respectively, which outperforms the traditional machine learning methods which rely on radiomics features and deep learning features alone.
format	article
author	Qian ShaoYutao Wang Rongrong Xuan Yutao Wang Jian Xu Menglin Ouyang Caoqian Yin Wei Jin
author_facet	Qian ShaoYutao Wang Rongrong Xuan Yutao Wang Jian Xu Menglin Ouyang Caoqian Yin Wei Jin
author_sort	Qian ShaoYutao Wang
title	Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI
title_short	Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI
title_full	Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI
title_fullStr	Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI
title_full_unstemmed	Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI
title_sort	deep learning and radiomics analysis for prediction of placenta invasion based on t2wi
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/98c2693fab7e4ddfbe8258cdec4b176d
work_keys_str_mv	AT qianshaoyutaowang deeplearningandradiomicsanalysisforpredictionofplacentainvasionbasedont2wi AT rongrongxuan deeplearningandradiomicsanalysisforpredictionofplacentainvasionbasedont2wi AT yutaowang deeplearningandradiomicsanalysisforpredictionofplacentainvasionbasedont2wi AT jianxu deeplearningandradiomicsanalysisforpredictionofplacentainvasionbasedont2wi AT menglinouyang deeplearningandradiomicsanalysisforpredictionofplacentainvasionbasedont2wi AT caoqianyin deeplearningandradiomicsanalysisforpredictionofplacentainvasionbasedont2wi AT weijin deeplearningandradiomicsanalysisforpredictionofplacentainvasionbasedont2wi
_version_	1718439607683514368

Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI

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