ABSTRACT
Objective A predictive model of WHO/ISUP grading of renal clear cell carcinoma was constructed based on CT radiomics.Methods The clinical data of 104 patients with ccRCC confirmed by operation or biopsy from March 2014 to December 2018 in the Mfiliated Hospital of Shaanxi University of Traditional Chinese Medicine were retrospectively analyzed.There were 70 males and 34 females,and the age was 61.2 ± 11.7 years.The patients were randomly divided into development cohort (73 cases) and validation cohort (31 cases) by stratified sampling according to 7∶3 ratio.According to the WHO/ISUP pathological grading criteria of renal cancer in 2016,Ⅰ and Ⅱ were defined as low-grade group,Ⅲ and Ⅳ were defined as high-grade group.The radiomics features of ccRCC were calculated in cortical phase images of CT enhanced scanning.LASSO regression was used to reduce the radiomics feature dimensionality in the training group,and to establish radiomics risk scores.The binary logistic regression was used to build the prediction model,which was used in the validation group.Bootstrap method was used to validate the model of training and validation group.AUC,sensitivity and specificity were calculated respectively.Hosmer-Lemeshow goodness-of-fit test was used to evaluate model calibration degree.Results After dimensionality reduction,the radiomics risk score of ccRCC was established.The low and high-level risk scores of the training group were-2.49 ± 1.73 and 1.23 ± 2.17,with significant difference (t =-7.785,P ＜ 0.01).The binary logistic regression multivariate analysis showed that the radiomics risk score was an independent risk factor in identifying low or high-grade ccRCC with odds ratio of (OR =3.576,95％ CI 1.964 ～ 6.513).The predictive model was Y =1/[1 + exp(-Z)],Z =1.274 × radiomics risk score + 0.072.The AUC of radiomics risk score in training group was 0.940 (95％ CI 0.883-0.998) with 95.5％ sensitivity and 88.2％ specificity after internal verification by Bootstrap method,and good Hosmer-Lemeshow goodness-of-fit test (x2 =4.463,P ＞ 0.05).The low and high-level risk scores of the Validation group were-2.27 ± 2.02 and 0.82 ± 2.08,with significant difference (t =-3.832,P ＜ 0.01).The AUC in validation group was 0.859(95％ CI 0.723-0.995) with 77.8％ sensitivity and 81.8％ specificity,and with good Hosmer-Lemeshow goodness-of-fit test (x2 =14.554,P =0.068) as well.Conclusions The prediction model based on CT radiomics has high accuracy in predicting high or low grade of ccRCC.
ABSTRACT
OBJECTIVE@#To investigate the suppression of MDR1 and P-glycoprotein induced by small interfering RNA and the restoration of sensitivity to chemotherapeutic drugs in multidrug-resistant hepatocellular carcinoma cell line Bel7402/5-Fu.@*METHODS@#MDR1j targeted small interfering RNA duplexes were introduced into multidrug-resistant hepatocellular carcinoma cell line Bel7402/5-Fu. The suppression of MDR1 and its gene product P-glycoprotein was examined by RT-PCR and Western blot. MTT assay was performed to measure the reverse effect of small interfering RNA based on the results of IC50. Cell apoptosis was assessed by flow cytometry after various cell lines were treated with chemotherapeutic drugs.@*RESULTS@#The overexpression of MDR1 and P-glycoprotein was suppressed efficiently by the introduction of small interfering RNA, which caused sequence-specific gene silence. The level of MDR1 in the transfected Bel7402/5-Fu cells reduced to 22.55% and P-glycoprotein to 25.49% compared with those of the controls. The apoptosis rate of Bel7402/5-Fu cells increased significantly in the siRNA group during the chemotherapy (P<0.01). Their resistance to 5-Fu was reversed by 14.88 folds, which indicated the restoration of sensitivity to drugs.@*CONCLUSION@#Small interfering RNA can inhibit MDR1 expression effective and reverse the multidrug resistance mediated by P-glycoprotein.