Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

PurposeTo explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer.MethodsThe clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive...

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Autores principales:	Yadi Zhu, Ling Yang, Hailin Shen
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	breast cancer radiomics sentinel lymph node metastasis machine learning CE-MRI Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/053bb0264936480c8a15f4e552a3bdde
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spelling	oai:doaj.org-article:053bb0264936480c8a15f4e552a3bdde2021-11-19T06:19:19ZValue of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer2234-943X10.3389/fonc.2021.757111https://doaj.org/article/053bb0264936480c8a15f4e552a3bdde2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.757111/fullhttps://doaj.org/toc/2234-943XPurposeTo explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer.MethodsThe clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (n=123) and validation set (n= 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated.ResultsThere is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set.ConclusionsWe revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients.Yadi ZhuLing YangHailin ShenFrontiers Media S.A.articlebreast cancerradiomicssentinel lymph node metastasismachine learningCE-MRINeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	breast cancer radiomics sentinel lymph node metastasis machine learning CE-MRI Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	breast cancer radiomics sentinel lymph node metastasis machine learning CE-MRI Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Yadi Zhu Ling Yang Hailin Shen Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
description	PurposeTo explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer.MethodsThe clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (n=123) and validation set (n= 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated.ResultsThere is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set.ConclusionsWe revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients.
format	article
author	Yadi Zhu Ling Yang Hailin Shen
author_facet	Yadi Zhu Ling Yang Hailin Shen
author_sort	Yadi Zhu
title	Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
title_short	Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
title_full	Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
title_fullStr	Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
title_full_unstemmed	Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
title_sort	value of the application of ce-mri radiomics and machine learning in preoperative prediction of sentinel lymph node metastasis in breast cancer
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/053bb0264936480c8a15f4e552a3bdde
work_keys_str_mv	AT yadizhu valueoftheapplicationofcemriradiomicsandmachinelearninginpreoperativepredictionofsentinellymphnodemetastasisinbreastcancer AT lingyang valueoftheapplicationofcemriradiomicsandmachinelearninginpreoperativepredictionofsentinellymphnodemetastasisinbreastcancer AT hailinshen valueoftheapplicationofcemriradiomicsandmachinelearninginpreoperativepredictionofsentinellymphnodemetastasisinbreastcancer
_version_	1718420294686736384

Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

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