Objective To evaluate the value of radiomics model in distinguishing grade Ⅱ and Ⅲ of gliomas from T2-weighted MRI images.Methods 159 gliomas patients(Mayo Clinic,October 2002-August 2011),who underwent non-enhanced MRI and tumor grades confirmation from the Cancer Genome Atla(TCIA)data portal,including grade Ⅱ(n=104)and Ⅲ(n=55)of gliomas.Patients were divided into training cohorts(n=111)and validation cohorts(n=48)in a ratio of 7:3.Gliomas were imported into the ITK-SNAP to manually delineate volume of interest(VOI)on T2-weighted images.The delineated data was imported into A.K software(Artificial Intelligence Kit v.3.1.0.R,GE Company)to extract tumor radiomics features.A total of 396 features were extracted,and the main features included 6 categories including Histogram,GLCM,GLSZM,RLM,Form Factor,Haralick.LASSO regression was used for feature screening.A formula was generated using a linear combination of selected features that were weighted by their respective LASSO coefficient.A radiomics score was calculated for each patient by the formula.The predictive accuracy of radiomics model was quantified by AUC in both cohorts.The calibration degree of the radiomics was evaluated by Hosmer-Lemeshow test.The clinical usefulness of the radiomics model was assessed by decision curve analysis.Results Four radiomics features were chosen to build a radiomics model that distinguished grade Ⅱ and Ⅲ of gliomas with an AUC,sensitivity,specificity and CD of 0.723,75％,89％,0.120 in training cohort;and 0.800,73％,82％,0.561 in the validation cohort,respectively.When the threshold probability of DCA is 0.17％-0.99％,the classification of lower grade glioma by radiomics model is better than that of all patients as grade Ⅱ and Ⅲ.Conclusion The radiomics model based on T2-weighted MRI images can distinguish grade Ⅱ and Ⅲ of lower grade gliomas,providing a non-invasive technique for developing a surgical plan and prognosis for gliomas patients.
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