Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer
Abstract Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pat...
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doaj-1965ce2462ab4deca5f503e9b47375312021-04-02T11:23:30ZengBMCCancer Imaging1470-73302020-09-012011810.1186/s40644-020-00342-xPharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancerMeijie Liu0Ning Mao1Heng Ma2Jianjun Dong3Kun Zhang4Kaili Che5Shaofeng Duan6Xuexi Zhang7Yinghong Shi8Haizhu Xie9School of Clinical Medicine, Binzhou Medical UniversityDepartment of Radiology, Yantai Yuhuangding HospitalDepartment of Radiology, Yantai Yuhuangding HospitalDepartment of Radiology, Yantai Yuhuangding HospitalDepartment of Radiology, Yantai Yuhuangding HospitalDepartment of Radiology, Yantai Yuhuangding HospitalGE HealthcareGE HealthcareDepartment of Radiology, Yantai Yuhuangding HospitalDepartment of Radiology, Yantai Yuhuangding HospitalAbstract Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. Results Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. Conclusions The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.http://link.springer.com/article/10.1186/s40644-020-00342-xBreast cancerSentinel lymph nodeMagnetic resonance imagingRadiomicsPharmacokinetic parameters |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Meijie Liu Ning Mao Heng Ma Jianjun Dong Kun Zhang Kaili Che Shaofeng Duan Xuexi Zhang Yinghong Shi Haizhu Xie |
spellingShingle |
Meijie Liu Ning Mao Heng Ma Jianjun Dong Kun Zhang Kaili Che Shaofeng Duan Xuexi Zhang Yinghong Shi Haizhu Xie Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer Cancer Imaging Breast cancer Sentinel lymph node Magnetic resonance imaging Radiomics Pharmacokinetic parameters |
author_facet |
Meijie Liu Ning Mao Heng Ma Jianjun Dong Kun Zhang Kaili Che Shaofeng Duan Xuexi Zhang Yinghong Shi Haizhu Xie |
author_sort |
Meijie Liu |
title |
Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer |
title_short |
Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer |
title_full |
Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer |
title_fullStr |
Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer |
title_full_unstemmed |
Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer |
title_sort |
pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced mri for the preoperative prediction of sentinel lymph node metastasis in breast cancer |
publisher |
BMC |
series |
Cancer Imaging |
issn |
1470-7330 |
publishDate |
2020-09-01 |
description |
Abstract Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. Results Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. Conclusions The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer. |
topic |
Breast cancer Sentinel lymph node Magnetic resonance imaging Radiomics Pharmacokinetic parameters |
url |
http://link.springer.com/article/10.1186/s40644-020-00342-x |
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