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Objective@#To explore the value of dynamic contrast-enhanced MRI (DCE-MRI) based radiomics model in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) of breast cancer.@*Methods@#In this retrospective study, 91 patients who had received NAC and had pathological response results were collected in Meizhou people′s hospital from January 2016 to August 2018. A primary cohort consisted of 63 patients and an independent validation cohort consisted of 28 patients. The patients were divided into pCR group of 23 cases and non-pathological complete response (Non-pCR) group of 68 cases. All the patients underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) before NAC. A list of radiomics features were extracted using the A.K software and the corresponding radiomics signature was constructed. Logistic regression was used to develop the prediction model. The predictive ability of the model was tested by using the area under the curve (AUC) of ROC analysis.@*Results@#The discrimination performance of radiomics signature yielded a AUC of 0.750 in the primary dataset and a AUC of 0.789 in the validation dataset. The model that incorporated estrogen receptor (ER), progesterone receptor (PR) and radiomics features was developed, and had an AUC of 0.859 in the primary dataset and an AUC of 0.905 in the validation dataset.@*Conclusion@#The radiomics predictive model, which integrated with the DCE-MRI based radiomics signature, ER and PR, can be used as a promising and applicable adjunct approach for predicting the pCR to NAC of breast cancer.
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Objective To explore the value of dynamic contrast-enhanced MRI (DCE-MRI) based radiomics model in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) of breast cancer. Methods In this retrospective study, 91 patients who had received NAC and had pathological response results were collected in Meizhou people′s hospital from January 2016 to August 2018. A primary cohort consisted of 63 patients and an independent validation cohort consisted of 28 patients. The patients were divided into pCR group of 23 cases and non-pathological complete response (Non-pCR) group of 68 cases. All the patients underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) before NAC. A list of radiomics features were extracted using the A. K software and the corresponding radiomics signature was constructed. Logistic regression was used to develop the prediction model. The predictive ability of the model was tested by using the area under the curve (AUC) of ROC analysis. Results The discrimination performance of radiomics signature yielded a AUC of 0.750 in the primary dataset and a AUC of 0.789 in the validation dataset. The model that incorporated estrogen receptor (ER), progesterone receptor (PR) and radiomics features was developed, and had an AUC of 0.859 in the primary dataset and an AUC of 0.905 in the validation dataset. Conclusion The radiomics predictive model, which integrated with the DCE-MRI based radiomics signature, ER and PR, can be used as a promising and applicable adjunct approach for predicting the pCR to NAC of breast cancer.
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Objective ToinvestigatethecorrelationandthediagnosticefficiencyofquantitativeDCE-MRIparametersandADC valueinhistopathologicalgradeinpatients withinvasiveductalbreastcancer.Methods The DCE-MRIquantitativeparameters (Ktrans,KepandVe),semiquantitativeparameters(W-in,W-outandTTP)andtheADCvaluewereanalyzedandcomparedaccording bydifferenthistopathologicalgradein90invasiveductalbreastcancerpatients.Results ThemeanvalueofKtrans washigheringradeⅢgroupthanthatingradeⅡgroup,andthemeanvalueofADCwasloweringradeⅢgroupthanthatingradeⅡgroup.Thedifferenceswere statisticallysignificant(P<0.05),butthecorrelationswereweak(|r|<0.30).TherewerenostatisticallysignificantdifferencesamongKep, Ve,W-in,W-out,TTPingradeⅡandgradeⅢ (P>0.05).TheAUCofKtrans,ADCandKtranscombinedwithADCwere0.647,0.685 and0.749,respectively.Conclusion TheDCE-MRIquantitativeparametersKtransandADCvaluehavecorrelationswithhistopathologicalgradeof invasiveductalbreastcancer.HigherKtransandlowerADCvalueindicatehigherhistologicalgrade,andKtranscombinedwithADCcould improvethediagnosticefficiency.