AUTHOR=You Wei , Mao Yitao , Jiao Xiao , Wang Dongcui , Liu Jianling , Lei Peng , Liao Weihua 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1083216

DOI=10.3389/fonc.2023.1083216

ISSN=2234-943X

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

Materials and Methods: Patients diagnosed with glioma from March 2017 to September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated but 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 radiomics features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and the 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, the proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomic or VASARI respectively with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, less than Model 1, which got an AUC of 0.966.

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