The value of multimodal MRI radiomics in predicting muscle-invasive bladder cancer
10.3969/j.issn.1002-1671.2024.02.017
- VernacularTitle:基于多模态MRI的影像组学预测膀胱癌肌层浸润的应用价值
- Author:
Yingsi YANG
1
;
Xi LONG
;
Xiaohong CHEN
;
Rihui YANG
;
Yuhui ZHANG
;
Weixiong FAN
;
Tianhui ZHANG
Author Information
1. 梅州市人民医院磁共振科,广东 梅州 514031
- Keywords:
bladder cancer;
magnetic resonance imaging;
radiomics
- From:
Journal of Practical Radiology
2024;40(2):249-252,274
- CountryChina
- Language:Chinese
-
Abstract:
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.
