Non-invasive prediction of 1p/19q deletion in diffuse lower grade gliomas based on MRI image features combined with machine learning algorithm

YAO Qiaoli; LIANG Zhiyu; DENG Kan; ZHU Liuhong; LIU Hao; XU Yikai

Abstract：Objective To explore the value of MRI image features combined with machine learning algorithm in predicting 1p/19q deletion in diffuse lower grade gliomas.Methods A retrospective collection of 79 cases of glioma confirmed by surgical pathology as grade Ⅱ～Ⅲ[39 cases of isocitrated dehydrogenase(IDH)mutation with 1p/19q co-deletion,40 cases of IDH mutation with 1p/19q non-co-deletion]was conducted and all patients underwent conventional head MRI scan and enhance-ment(T1WI,T2WI,SWI,FLAIR,DWI,CE-T1WI),and imaging features were extracted by neuroimaging radiologist with un-known pathological results as follows:calcification or hemorrhage,T2-FLAIR mismatch,peritumoral edema,degree of enhance-ment,T2 heterogeneity,cortical involvement,boundary rules,and midline bias.Chi-square test or Fisher's exact test was used to evaluate the statistical differences in imaging features between the two groups of gliomas and logistic regression analysis was performed on the image features.In addition,a machine learning model was constructed using the extracted MRI features,and the receiver operating characteristic(ROC)curve was used to analyze its diagnostic efficacy in predicting 1p/19q deletion.Re-sults The three imaging features of calcification or hemorrhage,T2-FLAIR mismatch sign,and T2 heterogeneity were statisti-cally different in different 1p/19q deletion states(P<0.05),combined with the above three imaging features,the area under the curve(AUC)of the logistic regression model could reach 0.859;in addition,the machine learning model constructed using MRI image features was more robust,and the test set AUC could be as high as 0.910.Conclusion The preoperative MRI features combined with machine learning algorithm can be used to predict 1p/19q deletion states in diffuse lower grade gliomas noninva-sively.