Objective To explore the value of diffusion tensor imaging analysis index along the perivascular space(DTI-ALPS)for assessing age-related functional changes in glymphatic system(GS).Methods Totally 27 healthy subjects from Meizhou People's Hospital and 100 healthy subjects from neuroimaging informatics tools and resources collaborator database who underwent T1-weighted magnetization-prepared rapid gradient echo(T1-MPRAGE)and DTI scanning were retrospectively enrolled and divided into youth group(n=38),middle-aged group(n=57)and elderly group(n=32).Automated DTI-ALPS index analysis procedure was used to minimize manual errors and derive DTI-ALPS index.The general data,neuropsychological assessment results and DTI-ALPS indices were compared among groups.Spearman correlation analysis was performed to observe the relationships of DTI-ALPS index and age,gender,as well as neuropsychological scores.Results The average age in youth group,middle-aged group and elderly group was(28.5±5.8),(53.7±6.8)and(73.8±2.3)years,respectively.No significant difference of DTI-ALPS index was found between middle-aged group and elderly group(P＞0.05),which were both lower than that in youth group(both P＜0.05).DTI-ALPS index was weakly negatively correlated with age(rs=-0.340,P＜0.001),but not significantly correlated with gender nor neuropsychological assessment results(both P＞0.05).Conclusion DTI-ALPS index was negatively correlated with age in healthy individuals,hence having potential utility for assessing age-related functional changes in GS.

Objective To explore the efficacy of a model combining clinicopathological characteristics and multiparametric MRI features for predicting BRCA-mutated breast cancer(BC).Methods A total of 256 BC patients were retrospectively selected and divided into BRCA mutation group(116 cases)and BRCA wild group(140 cases)based on the BRCA results.Chi-square tests or independ-ent sample t-tests were used to compare the differences in clinicopathological characteristics and multiparametric MRI features between the BRCA mutation group and the wild group.Risk factors for BRCA-mutated BC were identified through univariate and multivariate logistic regression ananlyses,and a combined predictive model was constructed.Receiver operating characteristic(ROC)curve was used to ana-lyze the diagnostic efficacy of the model.Results There were statistically significant differences in T stage,human epidermal growth factor receptor 2(HER-2),Ki-67,non-mass enhancement,enhancement pattern,time-signal intensity curve(TIC)type,and apparent diffusion coefficient(ADC)values between the BRCA mutation group and the wild group.Univariate logistic regression analysis showed that T stage,HER-2,Ki-67,non-mass enhancement,enhancement pattern,TIC type,and ADC values were risk factors for BRCA-mutated BC(P<0.05).Multivariate logistic regression analysis revealed that T stage,HER-2,Ki-67,enhancement pattern,and TIC type were independent risk factors for BRCA-mutated BC(P<0.05).The combined model incorporating T stage,HER-2,Ki-67,enhancement pattern,and TIC type had the best diagnostic efficacy in predicting BRCA-mutated BC,with an area under the curve(AUC)of 0.751.Conclusion The combined model integrating T stage,HER-2,Ki-67,enhancement pattern,and TIC type has good efficacy in predicting BRCA-mutated BC.

Objective To explore the preoperative non-invasive prediction of early postoperative glioma recurrence using multi-modal conventional MRI radiomics.Methods A retrospective analysis of the clinical and MRI data of 83 glioma patients who met the inclusion criteria was conducted.The Kruskal-Wallis test was used to compare clinical factors between recurrent and non-recurrent groups.The automated segmentation of the entire tumor lesion for glioma patients was accomplished with VB-Net algorithm,a deep learning approach developed by United Imaging Healthcare;and the extraction of radiomics features from preoperative T1CE and T2WI images was carried out on URP platform.The optimal feature combination was determined using the maximum relevance and minimum redundancy and least absolute shrinkage and selection operator methods.Logistic regression and five-fold cross-validation were employed to analyze radiomics features and construct 4 prediction models,namely T2WI model,T1CE model,T2WI+T1CE model,and imaging-clinical fusion model.The diagnostic performances of these models were evaluated and compared using the area under the receiver operating characteristic curve(AUC)and the Delong test.In addition,the model sensitivity and specificity were calculated.Results Postoperatively,there were 40 recurrent cases and 43 non-recurrent cases.The clinical factors such as glioma grade showed statistical significance between two groups(P<0.05),while gender and age did not show significant statistical differences(P>0.05).For the single-sequence radiomics models,T1CE model(AUC:0.804)outperformed T2WI model(AUC:0.702).The multi-modal combined model exhibited a higher AUC than the single-sequence prediction models,with an AUC of 0.849,a sensitivity of 72.5%,and a specificity of 79.1%.The imaging-clinical fusion model whose predictive efficiency was close to that of multi-modal combined model(P=0.303)also performed well in forecasting postoperative glioma recurrence,with an AUC of 0.839,a sensitivity of 72.5%,and a specificity of 79.1%.Conclusion The multi-modal conventional MRI radiomics model can better predict early postoperative glioma recurrence.The imaging-clinical fusion model that includes glioma grade does not have the diagnostic performance superior to that of radiomics model.

Objective To investigate the consistency and diagnostic efficacy of 1.5T and 3.0T MRI in the kidney clear cell likeli-hood score(ccLS)v2.0.Methods A retrospective collection was conducted on the data of 176 pathologically confirmed small renal mass(SRM).Two radiologists independently scored the MRI images using the ccLS v2.0.The Kappa test was used to assess inter-observer consistency,and receiver operating characteristic(ROC)curves were plotted to analyze the diagnostic efficacy.The area under the curve(AUC),sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NP V)were calculated.Results The inter-observer consistency for ccLS v2.0 score was good for 1.5T MRI and excellent for 3.0T MRI(Kappa values were 0.754 and 0.836,respectively).On 1.5T MRI examination,a ccLS≥4 points was identified as the optimal threshold,with AUC of 0.935 and 0.923 for the two radiologists,sensitivities of 92.45％and 88.68％,and specificities of 88.00％and 92.00％,respec-tively.For 3.0T MRI examination,using the same threshold,the AUC were 0.933 and 0.901,with sensitivities of 91.43％and 90.00％,and specificities of 78.57％and 78.57％for both radiologists.Conclusion The ccLS v2.0 demonstrates good consistency across high and low field intensity MRI,and a threshold of ccLS≥4 pionts provides high diagnostic efficacy for clear cell renal cell carcinoma(ccRCC).

Objective To investigate the value of predicting microsatellite instability(MSI)status in endometrial cancer based on whole-tumor apparent diffusion coefficient(ADC)histogram.Methods The data of 131 endometrial cancer patients who underwent preoperative MRI examination and were confirmed by pathology were retrospectively analyzed.According to the pathological immu-nohistochemical results,they were divided into microsatellite stability(MSS)group(103 cases)and MSI group(28 cases).The whole-tumor volume of interest(VOI)was outlined using ITK-SNAP software,and ADC histogram analysis was performed using uAI Research Portal software.The t-test or Mann-Whitney U-test were used to compare the differences between the two groups,and multifactorial logistic regression analysis was used to screen independent predictors for the above parameters with differences.The area under the curve(AUC),sensitivity and specificity were calculated using the receiver operating characteristic(ROC)curve.Results The ADC histogram parameters that were statistically significant between groups were ADC10th,ADC90th,ADCmaximum,ADCmedian,ADCmean,ADCrange,ADCinterquartile range,ADCuniformity,ADCvariance,ADCenergy,ADCentropy,ADCtotal energy,ADCroot mean square,ADCmean absolute deviation,ADCrobust mean absolute deviation,all the above parameters were significantly smaller in the MSI group than in the MSS group.Further multifactorial logistic regression analysis results showed that ADCmedian[odds ratio(OR)=1.019,P=0.020]and ADCroot mean square(OR=0.977,P=0.005)were independent predictors of the MSI status in endometrial cancer.The results of ROC curve showed that the AUC of ADCmedian and ADCroot mean square for predicting MSI status were 0.699 and 0.731,respectively,and the AUC of combining the two parameters to predict MSI status was 0.760,with a sensitivity of 57.14%and a specificity of 86.41%.Conclusion The parameters of ADCmedian and ADCroot mean square based on whole-tumor ADC histogram can be used to predict the MSI status of endometrial cancer,and the combined use of these two parameters helps to improve the efficacy of predicting MSI.

Objective To explore the efficacy of a model combining clinicopathological characteristics and multiparametric MRI features for predicting BRCA-mutated breast cancer(BC).Methods A total of 256 BC patients were retrospectively selected and divided into BRCA mutation group(116 cases)and BRCA wild group(140 cases)based on the BRCA results.Chi-square tests or independ-ent sample t-tests were used to compare the differences in clinicopathological characteristics and multiparametric MRI features between the BRCA mutation group and the wild group.Risk factors for BRCA-mutated BC were identified through univariate and multivariate logistic regression ananlyses,and a combined predictive model was constructed.Receiver operating characteristic(ROC)curve was used to ana-lyze the diagnostic efficacy of the model.Results There were statistically significant differences in T stage,human epidermal growth factor receptor 2(HER-2),Ki-67,non-mass enhancement,enhancement pattern,time-signal intensity curve(TIC)type,and apparent diffusion coefficient(ADC)values between the BRCA mutation group and the wild group.Univariate logistic regression analysis showed that T stage,HER-2,Ki-67,non-mass enhancement,enhancement pattern,TIC type,and ADC values were risk factors for BRCA-mutated BC(P<0.05).Multivariate logistic regression analysis revealed that T stage,HER-2,Ki-67,enhancement pattern,and TIC type were independent risk factors for BRCA-mutated BC(P<0.05).The combined model incorporating T stage,HER-2,Ki-67,enhancement pattern,and TIC type had the best diagnostic efficacy in predicting BRCA-mutated BC,with an area under the curve(AUC)of 0.751.Conclusion The combined model integrating T stage,HER-2,Ki-67,enhancement pattern,and TIC type has good efficacy in predicting BRCA-mutated BC.

Objective To investigate the consistency and diagnostic efficacy of 1.5T and 3.0T MRI in the kidney clear cell likeli-hood score(ccLS)v2.0.Methods A retrospective collection was conducted on the data of 176 pathologically confirmed small renal mass(SRM).Two radiologists independently scored the MRI images using the ccLS v2.0.The Kappa test was used to assess inter-observer consistency,and receiver operating characteristic(ROC)curves were plotted to analyze the diagnostic efficacy.The area under the curve(AUC),sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NP V)were calculated.Results The inter-observer consistency for ccLS v2.0 score was good for 1.5T MRI and excellent for 3.0T MRI(Kappa values were 0.754 and 0.836,respectively).On 1.5T MRI examination,a ccLS≥4 points was identified as the optimal threshold,with AUC of 0.935 and 0.923 for the two radiologists,sensitivities of 92.45％and 88.68％,and specificities of 88.00％and 92.00％,respec-tively.For 3.0T MRI examination,using the same threshold,the AUC were 0.933 and 0.901,with sensitivities of 91.43％and 90.00％,and specificities of 78.57％and 78.57％for both radiologists.Conclusion The ccLS v2.0 demonstrates good consistency across high and low field intensity MRI,and a threshold of ccLS≥4 pionts provides high diagnostic efficacy for clear cell renal cell carcinoma(ccRCC).

Objective To investigate the value of predicting microsatellite instability(MSI)status in endometrial cancer based on whole-tumor apparent diffusion coefficient(ADC)histogram.Methods The data of 131 endometrial cancer patients who underwent preoperative MRI examination and were confirmed by pathology were retrospectively analyzed.According to the pathological immu-nohistochemical results,they were divided into microsatellite stability(MSS)group(103 cases)and MSI group(28 cases).The whole-tumor volume of interest(VOI)was outlined using ITK-SNAP software,and ADC histogram analysis was performed using uAI Research Portal software.The t-test or Mann-Whitney U-test were used to compare the differences between the two groups,and multifactorial logistic regression analysis was used to screen independent predictors for the above parameters with differences.The area under the curve(AUC),sensitivity and specificity were calculated using the receiver operating characteristic(ROC)curve.Results The ADC histogram parameters that were statistically significant between groups were ADC10th,ADC90th,ADCmaximum,ADCmedian,ADCmean,ADCrange,ADCinterquartile range,ADCuniformity,ADCvariance,ADCenergy,ADCentropy,ADCtotal energy,ADCroot mean square,ADCmean absolute deviation,ADCrobust mean absolute deviation,all the above parameters were significantly smaller in the MSI group than in the MSS group.Further multifactorial logistic regression analysis results showed that ADCmedian[odds ratio(OR)=1.019,P=0.020]and ADCroot mean square(OR=0.977,P=0.005)were independent predictors of the MSI status in endometrial cancer.The results of ROC curve showed that the AUC of ADCmedian and ADCroot mean square for predicting MSI status were 0.699 and 0.731,respectively,and the AUC of combining the two parameters to predict MSI status was 0.760,with a sensitivity of 57.14%and a specificity of 86.41%.Conclusion The parameters of ADCmedian and ADCroot mean square based on whole-tumor ADC histogram can be used to predict the MSI status of endometrial cancer,and the combined use of these two parameters helps to improve the efficacy of predicting MSI.

Objective To explore the preoperative non-invasive prediction of early postoperative glioma recurrence using multi-modal conventional MRI radiomics.Methods A retrospective analysis of the clinical and MRI data of 83 glioma patients who met the inclusion criteria was conducted.The Kruskal-Wallis test was used to compare clinical factors between recurrent and non-recurrent groups.The automated segmentation of the entire tumor lesion for glioma patients was accomplished with VB-Net algorithm,a deep learning approach developed by United Imaging Healthcare;and the extraction of radiomics features from preoperative T1CE and T2WI images was carried out on URP platform.The optimal feature combination was determined using the maximum relevance and minimum redundancy and least absolute shrinkage and selection operator methods.Logistic regression and five-fold cross-validation were employed to analyze radiomics features and construct 4 prediction models,namely T2WI model,T1CE model,T2WI+T1CE model,and imaging-clinical fusion model.The diagnostic performances of these models were evaluated and compared using the area under the receiver operating characteristic curve(AUC)and the Delong test.In addition,the model sensitivity and specificity were calculated.Results Postoperatively,there were 40 recurrent cases and 43 non-recurrent cases.The clinical factors such as glioma grade showed statistical significance between two groups(P<0.05),while gender and age did not show significant statistical differences(P>0.05).For the single-sequence radiomics models,T1CE model(AUC:0.804)outperformed T2WI model(AUC:0.702).The multi-modal combined model exhibited a higher AUC than the single-sequence prediction models,with an AUC of 0.849,a sensitivity of 72.5%,and a specificity of 79.1%.The imaging-clinical fusion model whose predictive efficiency was close to that of multi-modal combined model(P=0.303)also performed well in forecasting postoperative glioma recurrence,with an AUC of 0.839,a sensitivity of 72.5%,and a specificity of 79.1%.Conclusion The multi-modal conventional MRI radiomics model can better predict early postoperative glioma recurrence.The imaging-clinical fusion model that includes glioma grade does not have the diagnostic performance superior to that of radiomics model.

Objective To explore the value of diffusion tensor imaging analysis index along the perivascular space(DTI-ALPS)for assessing age-related functional changes in glymphatic system(GS).Methods Totally 27 healthy subjects from Meizhou People's Hospital and 100 healthy subjects from neuroimaging informatics tools and resources collaborator database who underwent T1-weighted magnetization-prepared rapid gradient echo(T1-MPRAGE)and DTI scanning were retrospectively enrolled and divided into youth group(n=38),middle-aged group(n=57)and elderly group(n=32).Automated DTI-ALPS index analysis procedure was used to minimize manual errors and derive DTI-ALPS index.The general data,neuropsychological assessment results and DTI-ALPS indices were compared among groups.Spearman correlation analysis was performed to observe the relationships of DTI-ALPS index and age,gender,as well as neuropsychological scores.Results The average age in youth group,middle-aged group and elderly group was(28.5±5.8),(53.7±6.8)and(73.8±2.3)years,respectively.No significant difference of DTI-ALPS index was found between middle-aged group and elderly group(P＞0.05),which were both lower than that in youth group(both P＜0.05).DTI-ALPS index was weakly negatively correlated with age(rs=-0.340,P＜0.001),but not significantly correlated with gender nor neuropsychological assessment results(both P＞0.05).Conclusion DTI-ALPS index was negatively correlated with age in healthy individuals,hence having potential utility for assessing age-related functional changes in GS.