الملخص
Objective To investigate the value of multimodal MRI radiomics in predicting muscle-invasive bladder cancer.Methods A total of 178 patients with pathology diagnosis of bladder cancer were retrospectively collected,including 31 cases of muscle invasive bladder cancer(MIBC)and 147 cases of non-muscle invasive bladder cancer(NMIBC).Patients were randomly divided into training group and testing group at a ratio of 7︰3.The range of bladder tumors in T2WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)images were segmented as volume of interest(VOI)by using ITK-SNAP software.Radiomics features were extracted through A.K software.The optimal radiomics features were obtained through radiomics algorithm and least absolute shrinkage and selection operator(LASSO)method.Finally,the logistic regression analysis method and random forest model method were used to construct prediction models.The performance of prediction models was evaluated by the receiver operating characteristic(ROC)curve.Results This study constructed four groups of models containing T2WI prediction model,DWI prediction model,ADC prediction model,and T2WI+DWI+ADC prediction model.The area under the curve(AUC)of T2WI,DWI,and ADC prediction models for identifying MIBC and NMIBC were separately 0.920,0.914,and 0.954 in the training group while those were respectively 0.881,0.773,and 0.871 in the testing group.There was no statistical significance between T2WI,DWI,and ADC prediction models.In training and testing groups,the AUC of T2WI+DWI+ADC prediction model were respectively 0.959 and 0.909,which were higher than the single sequence prediction model.The sensitivity and specificity of the training group were 0.905 and 0.853 and the sensitivity and specificity of the testing group were 0.778 and 0.795.Conclusion MRI radiomics prediction model can effectively differentiate MIBC and NMIBC.The T2WI+DWI+ADC prediction model shows better prediction efficiency.
الملخص
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.
الملخص
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.
الملخص
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.