Study On The Relationship Between The Imaging Characteristics Of T2 FLAIR Sequence And The Immunohistochemical Typing Of Glioma

Glioma is the most common malignant tumor of the central nervous system(CNS).According to the World Health Organization(WHO)criteria,it can be divided into low-grade gliomas(LGGs,grade I or II)and high-grade gliomas(HGGs,grade ? or ?).HGGs which include glioblastoma(GBM)and anaplastic glioma(AG)account for 60?75%of all gliomas.The average annual incidence of GBM is 3.19/100,000.Due to the high invasive and proliferative potential,the 5-year survival rate of glioblastoma patients is less than 5%,and a median survival duration is approximately 15 months after diagnosis.30%of gliomas are LGGs,which is defined as hereditary heterogeneous tumors.Although the prognosis is relatively good,almost all LGGs eventually undergo malignant transformation.Accurate grading of glioma is meaningful for clinical value,but significantly different prognosis exists among individuals who were classified as the same WHO grade.It has been fully studied that gliomas with the same or similar histological characteristics may carry different molecular genetic information.In 2016,WHO updated the classification of glioma by combining molecular markers with histopathology.WHO emphasized the importance of molecular detection in the classification of CNS tumors.In the final histopathological report,molecular detection must be performed to make follow-up clinical decisions and the importance of molecular markers can be demonstrated.Ki-67 labelling index(Ki-67 Li),vimentin,CD34 and S-100 are vital biological behavior biomarkers.Ki-67 nuclear antigen only expresses in proliferating cells,which makes it a reliable avenue to rapidly evaluate the growth fraction of normal and abnormal cells.Vimentin has gained more attention as an emblematical biomarker for Epithelial-mesenchymal transition(EMT),a process of epithelial cells to mesenchymal cells trans-differentiation,and EMT has a close association with tumor motility and invasiveness.CD34is well known as an endothelial marker which presents positive staining in physiologic and pathologic vessels.CD34 is also considered to be an optimum marker of microvascular density studies because of its good immunoreactivity.S-100 is an acidic calcium binding protein found primarily in glial cells in brain and correlates with maturation of the nervous system.Pathological typing of immunohistochemical biomarkers based on tumor genetics is more accurate and individualized for disease diagnosis,management and prognosis prediction which has significant clinical value.At present,the pathological histology of glioma after surgical resection or biopsy is the golden standard for gliomas grading and immunohistochemical typing.However,it has some inadequacies such as invasiveness,untimely sampling,time-consuming,sampling errors,different histological interpretations and limitations of obtaining complete IHC data in clinical practice.Therefore,it is necessary to find an effective and non-invasive approach to classify different glioma immunohistochemical subtypes.Magnetic resonance image(MRI),a non-invasive tool,plays an important role in the diagnosis of glioma.Radiomics,a new research technology,works closely with image data and can extract high-throughput features from medical images.Radiomics features have been used to predict grades of gliomas and show good performance.The sequences of MRI in the previous studies include:conventional sequences such as T1WI,T2WI and Contrast-enhanced T1-weighted images(T1-CE),advanced MRI techniques such as diffusion-weighted imaging(DWI)and arterial spin labeling(ASL)etc.However,T2-weighted fluid-attenuated inversion recovery(T2 FLAIR)was studied rarely,let alone the glioma immunohistochemical typing research based on this.T2 FLAIR can capture some features in the non-enhanced state,and the visible tumor phenotypic characteristics can be systematically quantified.It limits free water and can clearly show bound water.At present,only Ki-67radiomics studies can be detected,but S-100,CD34 and vimentin have not been discussed yet.In this study,we proposed radiomics features and the binary logistic regression model based on them to predict the immunohistochemical typing of Ki-67,S-100,CD34 and vimentin,so as to provide a non-invasive,more accurate and personalized disease management for glioma patients.Part One Study on the relationship between the radiomics features and glioma grades based on T2 FLAIR.Objective:To explore the relationship between the high-order radiomics features and glioma grades based on T2 FLAIR,and to observe the performance of the model combined with clinical features.Methods:51 pathologically confirmed gliomas patients admitted to our hospital from March 2015 to June 2018 were retrospectively analyzed,and all T2 FLAIR imaging were collected.The volumes of interest(VOIs)were manually sketched and the radiomics features were extracted.Feature reduction was performed by ANOVA+Mann-Whiney,spearman correlation analysis,least absolute shrinkage and selection operator(LASSO)and Gradient descent algorithm(GBDT).SMOTE technique was used to solve the data bias between two groups.Comprehensive binary logistic regression models were established.Area under the ROC curves(AUC),sensitivity,specificity and accuracy were used to evaluate the predict performance of models.Models reliability were decided according to the standard net benefit of the decision curves.We analyzed the correlation of radiomics features and glioma grades.Results:1. A total of 396 features were obtained from 51 cases of data.Four features were included in the identification model finally.They were two GLCM features(Haralick Correlation?angle135?offset7 and Inverse Difference Moment?All Direction?offset4?SD)and two GLRLM features(Low Grey Level Run Emphasis?All Direction?offset1?SD and Short Run Emphasis?Direction?offset7).The proportion of GLCM and GLRLM in feature clusters was 100%.The chi-square value of fit-goodness in this model was 2.797,P=0.946,AUC:0.888,sensitivity:0.781,specificity:0.895.The calibration parameters were mean absolute error=0.023,quantile of absolute error=0.049.2.In addition,we found that the age was normal distribution(Shapiro-Wilk test,P=0.2353),and were statistically different between the high and low grades of glioma(P=0.015),while there was no statistical difference in gender between the two groups(P=0.489).Combining age and radiomics features could significantly improve the model performance.The Chi-square value of fit-goodness of this model was 3.477,P=0.901,AUC:0.929,sensitivity:0.938,specificity:0.789.Additionally,the calibration parameters were mean absolute error=0.028,quantile of absolute error=0.061.Part Two Study on the relationship between T2 FLAIR sequence parameters of glioma.Objective:To establish a logistic regression model for positive/negative identification of IHC by Ki-67,vimentin,S-100 and CD34,to verify the feasibility of predicting molecular typing of glioma by imaging characteristics.Methods:51 pathologically confirmed gliomas patients admitted to our hospital from March 2015 to June 2018 took T2 FLAIR imaging examination,and Ki-67,vimentin,S-100 and CD34 immunohistochemical data were collected.Four groups of logistic regression models for pathological biomarkers positive/negative differential diagnosis were established to verify the feasibility of predicting molecular typing of glioma by radiomics features.In our study,Ki-67 Li was divided into 4 levels according to the positive rate:0?5%was level 0,6?25%was level 1,26?50%was level 2,and more than50%was level 3.In this study,the Ki-67 cohort was divided into label 0 group(level 0 and level 1,negative expression)and label 1 group(level 2 and level 3,positive expression).It was also mentioned in the literature that the second Ki-67 classification method was that Ki-67 Li<20%was divided into label 0and?20%was divided into label 1.We also analyzed the second method.Results:1. Based on the immunohistochemical data of Ki-67,S-100,vimentin and CD34,logistic regression prediction of four models were established.There was no statistical difference in the distribution of age and gender among the four models(P>0.05).2. We proposed four comprehensive models in revealing immunohi-stochemical typing of Ki-67,S-100,vimentin and CD34.Ki-67 model was composed of five features—three GLCM,one Haralick and one GLRLM;S-100 model included five features—one Histogram,three GLCM and one GLRLM;And vimentin radiomics model enrolled three features—one GLCM and two GLRLM;CD34 model was composed of three features—one GLCM and two GLRLM.Form Factor features were not included in the four models,GLCM and GLRLM were included in each model,and the ratio of them was relatively high in the corresponding feature clusters(Ki-67:60%;S-100:80%;vimentin:100%;CD34:100%).The low correlation coefficients between the16 features indicated little redundancy among every feature cluster.It also suggested that the information and predictive effects provided by single radiomics feature were independent and unique.3. Radscore of each model were significantly different in two labels(all P<0.05,Kruskal-Wallis H test).Therefore,the values of Radscore can be used as a significant factor in immunohistochemical classification in all four models.4. Hosmer-Lemeshow tests were conducted for fit-goodness testing of four models.The?~2values of Ki-67,S-100,vimentin and CD34 were 2.975,2.489,6.833 and 9.214,respectively.P values were 0.936,0.928,0.555 and0.325,respectively.Results showed that there was no significant difference between the four classification models and the corresponding actual models.Among them,the S-100 model and the actual model had the best fit-goodness.In addition,the Akaike information criterion(AIC)of Ki-67,S-100,vimentin and CD34 models were 72.509,46.163,45.037 and 56.654,respectively.The results showed that the fit-goodness of S-100 and vimentin models were better than that of Ki-67 and CD34 models.However,the specific reasons for the relative unreliability of Ki-67 and CD34 models need to be further verified.In addition,the S-100 model had the highest positive likelihood(9.38)ratio and the smallest negative likelihood ratio(0.12),indicating that the probability of the correct judgement using model when predicting the positive and negative expression of S-100 protein was much greater than the wrong judgment.Both higher positive predictive values(92.6)and negative predictive values(82.4)also indicate higher accuracy for S-100 model predictions.The high predictive performance was followed by vimentin model.However,in the Ki-67 and CD34 models,the predicting performance were relatively poor in terms of comprehensive indicators.5. Four clusters of significant features were screened out and four predicting models were constructed.AUC of Ki-67,S-100,vimentin and CD34 models were 0.713,0.923,0.854 and 0.745,respectively.The sensitivities were 0.692,0.893,0.875 and 0.556,respectively.The specificities were:0.667,0.905,0.722,and 0.875,with accuracy of 0.660,0.898,0.738,and 0.667,respectively.According to the decision curves,the Ki-67,S-100and vimentin models had reference values.6. When Ki-67 Li<20%was classified as negative and?20%was classified as positive,the Ki-67 model was tested by the Hosmer-Lemeshow goodness-of-fit test,?~2was 7.779 and P value was 0.455,AIC value was45.124,AUC was 0.916,sensitivity was 0.849,specificity was 0.882,and accuracy was 0.820.Part Three Radiomics features of solid tumor and edema in grade ? and ? glioma through T1-CE imagingObjective:To compare and analyze radiomics features of solid tumor and edema in grade ? and ? glioma through T1-CE imaging.Methods:Among the 51 patients collected above,23 cases were examined for T1-CE.ITK-SNAP software was used to sketch the enhanced solid tumor and edema area in T1-CE imaging of grade ? and grade ? glioma,and we compared the radiomics features of solid tumor and edema in grade ? and ? glioma through T1-CE imaging after feature screening.Results:1.Feature clusters were extracted from solid tumor for distinguishing grade ? and ? glioma,containing one histogram feature,one morphological feature(Compactness 1),three GLCM(Cluster Prominence?angle90?offset7,GLCMEntropy?All Direction?offset1,GLCMEropnty?angle0?offset4)and one GLRLM Long Run Emphasis?angle90?offset1).Six features were screened out,in which GLCM and GLRLM account for 66.7%of the feature clusters.2.In addition,feature clusters extracted from peritumoral edema includedonehistogramenergy,twoGLCM(Cluster Prominence?All Direction?offset1?SD,GLCMEntropy?angle135?offset1)and two GLRLM(Long Run Emphasis?angle135?offset7,Short Run High Grey Level Emphasis?All Direction?offset7?SD),similarly,GLCM and GLRLM account for 80%.3.It can be seen from the histogram of characteristic distribution of solid tumors and edema that the uniformity,kurtosis and skewness of feature distribution are quite different between grade ? and ? glioma.Conclusion:Overall,our results show that radiomics features are significantly correlated with glioma grade,combining age and radiomics features can significantly improve the model performance.In this study,radiomics features and the binary logistic regression model can predict the immunohistochemical typing of Ki-67,S-100,CD34 and vimentin,so as to provide a non-invasive,more accurate and personalized disease management for glioma patients,while the CD34 model was weaker for positive and negative discrimination,which may be related to the data distribution included in this study.Moreover,it can be seen from the graph that the uniformity,kurtosis and skewness of feature distribution are quite different between grade ? and ? glioma.In solid tumors and edema areas,the proportion of GLCM and GLRLM in the characteristic clusters that can distinguish grade ? and grade ? glioma is higher.And we have found that when Ki-67 Li is classified as negative/positive by 20%,the prediction effect of Ki-67 model is higher than that by 25%.