Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

ObjectiveTo build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer.Materials and Methods266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed diagnosis were analyzed. Training dataset had 146...

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Autores principales:	You-Fan Zhao, Zhongwei Chen, Yang Zhang, Jiejie Zhou, Jeon-Hor Chen, Kyoung Eun Lee, Freddie J. Combs, Ritesh Parajuli, Rita S. Mehta, Meihao Wang, Min-Ying Su
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	breast neoplasms diagnosis radiomics machine learning magnetic resonance imaging mammography Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/cf808bd0a71f4452b9e84eab9a3a47a3
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spelling	oai:doaj.org-article:cf808bd0a71f4452b9e84eab9a3a47a32021-11-17T04:29:55ZDiagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography2234-943X10.3389/fonc.2021.774248https://doaj.org/article/cf808bd0a71f4452b9e84eab9a3a47a32021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.774248/fullhttps://doaj.org/toc/2234-943XObjectiveTo build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer.Materials and Methods266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed diagnosis were analyzed. Training dataset had 146 malignant and 56 benign, and testing dataset had 48 malignant and 18 benign lesions. Fuzzy-C-means clustering algorithm was used to segment the enhanced lesion on subtraction MRI maps. Two radiologists manually outlined the corresponding lesion on mammography by consensus, with the guidance of MRI maximum intensity projection. Features were extracted using PyRadiomics from three DCE-MRI parametric maps, and from the lesion and a 2-cm bandshell margin on mammography. The support vector machine (SVM) was applied for feature selection and model building, using 5 datasets: DCE-MRI, mammography lesion-ROI, mammography margin-ROI, mammography lesion+margin, and all combined.ResultsIn the training dataset evaluated using 10-fold cross-validation, the diagnostic accuracy of the individual model was 83.2% for DCE-MRI, 75.7% for mammography lesion, 64.4% for mammography margin, and 77.2% for lesion+margin. When all features were combined, the accuracy was improved to 89.6%. By adding mammography features to MRI, the specificity was significantly improved from 69.6% (39/56) to 82.1% (46/56), p<0.01. When the developed models were applied to the independent testing dataset, the accuracy was 78.8% for DCE-MRI and 83.3% for combined MRI+Mammography.ConclusionThe radiomics model built from the combined MRI and mammography has the potential to provide a machine learning-based diagnostic tool and decrease the false positive diagnosis of contrast-enhanced benign lesions on MRI.You-Fan ZhaoZhongwei ChenYang ZhangJiejie ZhouJeon-Hor ChenJeon-Hor ChenKyoung Eun LeeFreddie J. CombsRitesh ParajuliRita S. MehtaMeihao WangMin-Ying SuMin-Ying SuFrontiers Media S.A.articlebreast neoplasmsdiagnosisradiomicsmachine learningmagnetic resonance imagingmammographyNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	breast neoplasms diagnosis radiomics machine learning magnetic resonance imaging mammography Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	breast neoplasms diagnosis radiomics machine learning magnetic resonance imaging mammography Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 You-Fan Zhao Zhongwei Chen Yang Zhang Jiejie Zhou Jeon-Hor Chen Jeon-Hor Chen Kyoung Eun Lee Freddie J. Combs Ritesh Parajuli Rita S. Mehta Meihao Wang Min-Ying Su Min-Ying Su Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography
description	ObjectiveTo build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer.Materials and Methods266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed diagnosis were analyzed. Training dataset had 146 malignant and 56 benign, and testing dataset had 48 malignant and 18 benign lesions. Fuzzy-C-means clustering algorithm was used to segment the enhanced lesion on subtraction MRI maps. Two radiologists manually outlined the corresponding lesion on mammography by consensus, with the guidance of MRI maximum intensity projection. Features were extracted using PyRadiomics from three DCE-MRI parametric maps, and from the lesion and a 2-cm bandshell margin on mammography. The support vector machine (SVM) was applied for feature selection and model building, using 5 datasets: DCE-MRI, mammography lesion-ROI, mammography margin-ROI, mammography lesion+margin, and all combined.ResultsIn the training dataset evaluated using 10-fold cross-validation, the diagnostic accuracy of the individual model was 83.2% for DCE-MRI, 75.7% for mammography lesion, 64.4% for mammography margin, and 77.2% for lesion+margin. When all features were combined, the accuracy was improved to 89.6%. By adding mammography features to MRI, the specificity was significantly improved from 69.6% (39/56) to 82.1% (46/56), p<0.01. When the developed models were applied to the independent testing dataset, the accuracy was 78.8% for DCE-MRI and 83.3% for combined MRI+Mammography.ConclusionThe radiomics model built from the combined MRI and mammography has the potential to provide a machine learning-based diagnostic tool and decrease the false positive diagnosis of contrast-enhanced benign lesions on MRI.
format	article
author	You-Fan Zhao Zhongwei Chen Yang Zhang Jiejie Zhou Jeon-Hor Chen Jeon-Hor Chen Kyoung Eun Lee Freddie J. Combs Ritesh Parajuli Rita S. Mehta Meihao Wang Min-Ying Su Min-Ying Su
author_facet	You-Fan Zhao Zhongwei Chen Yang Zhang Jiejie Zhou Jeon-Hor Chen Jeon-Hor Chen Kyoung Eun Lee Freddie J. Combs Ritesh Parajuli Rita S. Mehta Meihao Wang Min-Ying Su Min-Ying Su
author_sort	You-Fan Zhao
title	Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography
title_short	Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography
title_full	Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography
title_fullStr	Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography
title_full_unstemmed	Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography
title_sort	diagnosis of breast cancer using radiomics models built based on dynamic contrast enhanced mri combined with mammography
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/cf808bd0a71f4452b9e84eab9a3a47a3
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Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

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