BACKGROUND AND PURPOSE: Isocitrate dehydrogenase (IDH) wild-type lower-grade gliomas (histologic grades II and III) with epidermal growth factor receptor (EGFR) amplification or telomerase reverse transcriptase (TERT) promoter mutation are reported to behave similar to glioblastoma. We aimed to evaluate whether MR imaging features could identify a subset of IDH wild-type lower-grade gliomas that carry molecular features of glioblastoma. MATERIALS AND METHODS:In this multi-institutional retrospective study, pathologically confirmed IDH 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 EGFR amplification and TERT 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 ¼ 0.854) than that with only clinical features or Visually AcceSAble Rembrandt Images (areas under the curve ¼ 0.514 and 0.648, respectively; P , . 001, both). When both Visually AcceSAble Rembrandt Images and radiomics were added to clinical features, the predictive performance significantly increased (areas under the curve ¼ 0.514 versus 0.863, P , .001).CONCLUSIONS: MR imaging features integrated with machine learning classifiers may predict a subset of IDH wild-type lowergrade gliomas that carry molecular features of glioblastoma.ABBREVIATIONS: AUC ¼ area under the receiver operating characteristic curve; cIMPACT-NOW ¼ Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy; GBM ¼ glioblastoma; LASSO ¼ least absolute shrinkage and selection operator; RFE ¼ recursive feature elimination; SVM ¼ support vector machine; TCGA ¼ The Cancer Genome Atlas; VASARI ¼ Visually AcceSAble Rembrandt Images; WHO ¼ World Health Organization A mutation in the isocitrate dehydrogenase (IDH) gene is a major classifier that leads to the stratification of gliomas with significantly different survival rates among the lower-grade gliomas (World Health Organization [WHO] grades II and III) as well as glioblastomas (GBMs). 1-4 IDH wild-type tumors, which account for ,30% of the histologic grade II and III gliomas, show worse prognoses than those with the IDH mutation. 1,5,6 Previous studies have reported heterogeneous clinical outcomes among the IDH wild-type lower-grade gliomas according to a variable combination of genetic profiles. [7][8][9] Recently, the Consortium to Inform Molec...

BACKGROUND AND PURPOSE: Differentiating glioblastoma from solitary brain metastasis preoperatively using conventional MR images is challenging. Deep learning models have shown promise in performing classification tasks. The diagnostic performance of a deep learning-based model in discriminating glioblastoma from solitary brain metastasis using preoperative conventional MR images was evaluated. MATERIALS AND METHODS: Records of 598 patients with histologically confirmed glioblastoma or solitary brain metastasis at our institution between February 2006 and December 2017 were retrospectively reviewed. Preoperative contrastenhanced T1WI and T2WI were preprocessed and roughly segmented with rectangular regions of interest. A deep neural network was trained and validated using MR images from 498 patients. The MR images of the remaining 100 were used as an internal test set. An additional 143 patients from another tertiary hospital were used as an external test set. The classifications of ResNet-50 and 2 neuroradiologists were compared for their accuracy, precision, recall, F1 score, and area under the curve. RESULTS: The areas under the curve of ResNet-50 were 0.889 and 0.835 in the internal and external test sets, respectively. The area under the curve of neuroradiologists 1 and 2 were 0.889 and 0.768 in the internal test set and 0.857 and 0.708 in the external test set, respectively. CONCLUSIONS: A deep learning-based model may be a supportive tool for preoperative discrimination between glioblastoma and solitary brain metastasis using conventional MR images. ABBREVIATIONS: CE ¼ contrast enhanced; GBM ¼ glioblastoma; ROC ¼ receiver operating characteristic; AUC ¼ area under the curve