| Part ?:Application of a simplified method for estimating perfusion derived from diffusion-weighted MR imaging in glioma gradingObjective:To evaluate the feasibility of a simplified method based on diffusion-weighted imaging(DWI)acquired with three b-values to measure tissue perfusion linked to microcirculation,to validate it against from perfusion-related parameters derived from intravoxel incoherent motion(IVIM)and dynamic contrast-enhanced(DCE)magnetic resonance(MR)imaging,and to investigate its utility to differentiate low-from high-grade gliomas.Materials and Methods:The prospective study was approved by the local institutional review board and written informed consent was obtained from all patients.From May 2016 and May 2017,50 patients confirmed with glioma were assessed with multib-value DWI and DCE-MR imaging at 3.0 T.Besides conventional apparent diffusion coefficient(ADC0,1000)map,perfusion-related parametric maps for IVIM-derived perfusion fraction(f)and pseudodiffusion coefficient(D*),DCE-MR imaging-derived pharmacokinetic metrics,including Ktrans,Ve and Vp,as well as a metric named simplified perfusion fraction(SPF),were generated.Correlation between perfusion related parameters was analyzed by using the Spearman rank correlation.All imaging parameters were compared between the low-grade(n=19)and high-grade(n=31)groups by using the Mann-Whitney U test.The diagnostic performance for tumor grading was evaluated with receiver operating characteristic(ROC)analysis.Results:SPF showed strong correlation with IVIM-derivedf and D*(?=0.732 and 0.716,respectively;both P<0.001).Compared with f,SPF was more correlated with DCE MR imaging-derived Ktrans(?=0.607;P<0.001)and vp(?=0.397;P=0.004).Among all parameters,SPF achieved the highest accuracy for differentiating low-from high-grade gliomas,with an area under the ROC curve value of 0.942,which was significantly higher than that of ADC0,1000(P=0.004).By using SPF as a discriminative index,the diagnostic sensitivity and specificity were 87.1%and 94.7%,respectively,at the optimal cut-off value of 19.26%.Conclusions:The simplified method to measure tissue perfusion based on DWI by using three b-values may be helpful to differentiate low-from high-grade gliomas.SPF may serve as a valuable alternative to measure tumor perfusion in gliomas in a noninvasive,convenient and efficient way.Part ?:Brain T1? mapping for grading and IDH-1 gene mutation detection of gliomas:a preliminary studyObjective:The longitudinal relaxation time in the rotating frame(T1?)has proved to be sensitive to metabolism and useful in application to neurodegenerative diseases.However,few literature exists on its utility in gliomas.Thus,this study was conducted to explore the performance of T1?mapping in tumor grading and characterization of isocitrate dehydrogenase 1(IDH-1)gene mutation status of gliomas.Materials and Methods:Between May 2016 and March 2017,patients with suspected glioma who were scheduled for neurosurgical resection were enrolled in this study.Fifty-seven patients with gliomas underwent brain MRI and quantitative measurements of T1? were recorded.Parameters were compared between high-grade gliomas(HGG)and low-grade gliomas(LGG)and between IDH-1 mutant and wildtype groups.Results:HGG showed significantly higher T1? values in both the solid and peritumoral edema areas compared with LGG(P<0.001 and P=0.005,respectively).Receiver operating characteristic(ROC)curve analysis showed that T1? value in the solid area achieved the highest area under the ROC curve(AUC,0.841)in grading with a sensitivity of 80.6%and a specificity of 81.0%.In the grade ?/? glioma group,multivariate logistic regression showed that both tumor frontal lobe location(odds ratio[OR]526.608;P=0.045)and T1? value of the peritumoral edema area(OR 0.863;P=0.037)were significant predictors of IDH-1 mutation.Using the combination,the diagnostic sensitivity and specificity for IDH-1 mutated gliomas were 93.3%and 88.9%,respectively.Conclusion:Our study shows the feasibility of applying T1?mapping in assessing the histologic grade and IDH-1 mutation status of gliomas.Part ?:Predicting IDH-1 gene mutation in lower-grade glioma based on the qualitative and quantitative features of conventional MR imagingObjective:Lower-grade glioma(LrGG)is a class of gliomas with lower invasiveness and relatively better prognosis than glioblastoma,and LrGG with isocitrate dehydrogenase(IDH)gene mutation has better prognosis and longer survival.This study aimed to noninvasiviely predict IDH-1 genotype of LrGG based on VASARI(Visually Accessible Rembrandt Images)features and ADC(apparent diffusion coefficient)radiomics features by using machine learning method.Materials and Methods:This study was a retrospective study.A total of 102 LrGG cases with IDH-1 genotype results and preoperative MR examinations were collected.The VASARI features of the tumor were extracted from T1-weighted imaging,T2-weighted imaging,T2 FLAIR imaging and contrast enhanced T1-weighted imaging.After segmentation of the tumor,56 radiomics features of the tumor area on the ADC map were extracted.The feature selection was performed by the maximum correlation minimum redundancy algorithm combined with the 0.632 bootstrap method.The random forest algorithm was used for classification,and the area under the curve(AUC)was used as the evaluation index.The classification model was established by combining the optimal 5 VASARI features and 10 radiomics features respectively.The VASARI-based optimal classification model and the radiomics-based optimal classification model were combined to judge whether the two had better classification effect.Further,the age and gender of patients were added to the model to determine whether it could contribute to the improvement of the model classification performance.The validation set was used to verify the classification effect of the model.All statistics were performed using Matlab software and SPSS software.The discrete variables were compared using chi-square test and the continuous variables were compared using independent sample t-test.A P value<0.05 was considered to be a significant difference.Results:According to the AUC size ranking,the top 5 VASARI features with the best predictive performance were:enhancement quality(AUC=0.752),deep white matter invasion(AUC=0.738),tumor location(AUC)=0.684),necrosis proportion(AUC=0.682)and T1/FLAIR ratio(AUC=0.632).On the training set,feature combination 3,namely GLRLM.SRLGE,GLRLM.LGRE and GLRLM.RLV,obtained the maximum AUC(0.849),and the sensitivity,specificity and accuracy were 0.790,0.770 and 0.789,respectively.On the validation set,the feature combination 10,that is,all 10 features,obtained an AUC of 0.849,and the sensitivity,specificity,and accuracy were 0.724,0.761,and 0.743,respectively.VASARI features(enhancement quality)combined with radiomics features(GLRLM.SRLGE,GRLLM.LGRE,GLRLM.RLV,Histogram.Min,Eccentricity,GLSZM.LZHGE,GLSZM.LGZE,Histogram.Energy,Histogram.Std,and GLSZM.ZSN)obtained a classification AUC of 0.879 on the validation set,with sensitivity,specificity and accuracy of 0.765,0.778 and 0.771,respectively.Incorporating age and gender into the model did not improve the classification effect.At this time,the AUC was 0.859,and the sensitivity,specificity and accuracy were 0.759,0.778 and 0.769,respectively.Conclusion:The results of this study indicate that VASARI features based on conventional magnetic resonance imaging and ADC-based imaging omics can non-invasively evaluate the IDH-1 genotype of LGG,and the combination of the two can improve the accuracy of diagnosis. |
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