Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images

Abstract

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 contrast-enhanced 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.

FIG 1.

Flow chart showing the patient population in the internal and external cohorts.

FIG 2.

Diagram of overall workflow.

FIG 3.

ROC curve for differentiating GBM and metastasis in the internal test set (AUC, 0.881; optimal cutoff for predictive index, 0.55).

FIG 4.

Images of a 65-year-old woman with history of recurrent ovarian cancer and pathologically proved brain metastasis. Contrast-enhanced T1WI (A) shows a heterogeneously enhancing mass in the left cerebellum. B, T2WI shows a perilesional T2 hyperintensity area surrounding the enhancing portion. C, Corresponding class activation maps show that the ResNet-50 model is referring to the mass and perilesional T2 hyperintensity area as well as the surrounding posterior fossa structures. ResNet-50 and both radiologists all correctly classified this lesion as brain metastasis.

FIG 5.

Images of a 62-year-old woman with pathologically proved GBM. CE T1WI (A) and T2WI (B) show an enhancing mass in the left frontal lobe with surrounding perilesional T2 hyperintensity area. Whereas ResNet-50 and radiologist 1 correctly classified this mass as GBM, radiologist 2 misclassified this lesion as metastasis. Corresponding class activation map (C) shows that ResNet-50 is referring to the enhancing portion as well as the surrounding peritumoral T2 hyperintensity areas.