Multiparameter MRI radiomcs and machine learning-based models can accurately predict triple-negative breast cancer
QI Xuan
WANG Wuling
YANG Hongkai
CHENG Weiqun
ZHAI Chengfeng
YANG Xin
DUAN Shaofeng
HE Yongsheng
Abstract：Objective To establish a predictive model by extracting radiomic features from multi-parametric MRI data and combining them with clinical characteristics,and identify the machine learning model with the highest predictive value for triple-negative breast cancer(TNBC).Methods A total of 175 breast cancer patients,including 40 cases of TNBC and 135 cases of non-triple negative breast cancer(NTNBC),were collected and divided into training set(n=123)and validation set(n=52)according to 7:3.Multiparameter predictive models were developed using various machine learning algorithms and combined with clinical features for joint modeling.The predictive performance of different models was assessed using ROC curves.Results In the training and validation sets,Boundary,WHO classification and T2WI signals of lesions were statistically different in TNBC and NTNBC(P<0.05),among the nine models established using rbf_SVM,including Model-T2WI,Model-DWI,Model-DCEPhase2,Model-DCEPhase7,Model-T2WI+DWI,Model-DCEPhase7+T2WI,Model-DCEPhase7+T2WI+DWI,and Model-DCEPhase7+T2WI+DWI+Clinic,the radiomics-based predictive model of Model-DCEPhase7+T2WI+DWI+Clinic demonstrated the highest performance,with areas under the curve(AUC)of 0.992 and 0.936 in the training and validation sets,respectively.Conclusion The radiomics model based on multi-parametric MRI can accurately predict TNBC,contributing to the clinical diagnosis and treatment management of TNBC.
Keywords：breast cancertriple negative breast cancerradiomicsmulti-parametric magnetic resonance imaging
Publication Date：2025-01-27
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 82-90 )