The combination of radiomics features and VASARI standard to predict glioma grade

Wei You; Yitao Mao; Xiao Jiao; Dongcui Wang; Jianling Liu; Peng Lei; Weihua Liao

doi:10.3389/fonc.2023.1083216

The combination of radiomics features and VASARI standard to predict glioma grade

Front Oncol. 2023 Mar 22:13:1083216. doi: 10.3389/fonc.2023.1083216. eCollection 2023.

Authors

Wei You¹, Yitao Mao¹, Xiao Jiao¹, Dongcui Wang¹, Jianling Liu¹, Peng Lei¹, Weihua Liao^{1

2

3

4}

Affiliations

¹ Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
² National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China.
³ National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
⁴ Molecular Imaging Research Center, Central South University, Changsha, China.

Abstract

Background and purpose: Radiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods.

Materials and methods: Patients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation.

Results: Our study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966.

Conclusion: The combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.

Keywords: VASARI; glioma grade; high-grade glioma; nomogram; pre-operation diagnosis; radiomics.