MRI Features May Predict Molecular Features of Glioblastoma in Wild-Type Lower-Grade Gliomas.

Background And Purpose: () wild-type lower-grade gliomas (histologic grades II and III) with () amplification or () promoter mutation are reported to behave similar to glioblastoma. We aimed to evaluate whether MR imaging features could identify a subset of wild-type lower-grade gliomas that carry molecular features of glioblastoma.

Materials And Methods: In this multi-institutional retrospective study, pathologically confirmed wild-type lower-grade gliomas from 2 tertiary institutions and The Cancer Genome Atlas constituted the training set (institution 1 and The Cancer Genome Atlas, 64 patients) and the independent test set (institution 2, 57 patients). Preoperative MRIs were analyzed using the Visually AcceSAble Rembrandt Images and radiomics. The molecular glioblastoma status was determined on the basis of the presence of amplification and promoter mutation. Molecular glioblastoma was present in 73.4% and 56.1% in the training and test sets, respectively. Models using clinical, Visually AcceSAble Rembrandt Images, and radiomic features were built to predict the molecular glioblastoma status in the training set; then they were validated in the test set.

Results: In the test set, a model using both Visually AcceSAble Rembrandt Images and radiomic features showed superior predictive performance (area under the curve = 0.854) than that with only clinical features or Visually AcceSAble Rembrandt Images (areas under the curve = 0.514 and 0.648, respectively;  < . 001, both). When both Visually AcceSAble Rembrandt Images and radiomics were added to clinical features, the predictive performance significantly increased (areas under the curve = 0.514 versus 0.863,  < .001).

Conclusions: MR imaging features integrated with machine learning classifiers may predict a subset of wild-type lower-grade gliomas that carry molecular features of glioblastoma.