Background Differentiation between glioblastoma and brain metastasis is highly important due to differing medical treatment strategies. While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between glioblastoma and solitary brain metastasis may be challenging due to their similar appearance on MRI. Purpose To differentiate between glioblastoma and brain metastasis subtypes using radiomics analysis based on conventional post‐contrast T1‐weighted (T1W) MRI. Study Type Retrospective. Subjects Data were acquired from 439 patients: 212 patients with glioblastoma and 227 patients with brain metastasis (breast, lung, and others). Field Strength/Sequence Post‐contrast 3D T1W gradient echo images, acquired with 1.5 and 3.0 T MR systems. Assessment Analysis included image preprocessing, segmentation of tumor area, and features extraction including: patients' clinical information, tumor location, first‐ and second‐order statistical, morphological, wavelet features, and bag‐of‐features. Following dimension reduction, classification was performed using various machine‐learning algorithms including support‐vector machine (SVM), k‐nearest neighbor, decision trees, and ensemble classifiers. Statistical Tests For classification, the data were divided into training (80%) and testing datasets (20%). Following optimization of the classifiers, mean sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. Results For the testing dataset, the best results for differentiation of glioblastoma from brain metastasis were obtained using the SVM classifier with mean accuracy = 0.85, sensitivity = 0.86, specificity = 0.85, and AUC = 0.96. The best classification results between glioblastoma and brain metastasis subtypes were obtained using SVM classifier with mean accuracy = 0.85, 0.89, 0.75, 0.90; sensitivity = 1.00, 0.60, 0.57, 0.11; specificity = 0.76, 0.92, 0.87, 0.99; and AUC = 0.98, 0.81, 0.83, 0.57 for the glioblastoma, breast, lung, and other brain metastases, respectively. Data Conclusion Differentiation between glioblastoma and brain metastasis showed a high success rate based on postcontrast T1W MRI. Classification between glioblastoma and brain metastasis subtypes may require additional MR sequences with other tissue contrasts. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:519–528.

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In recent years, several automatic methods have been proposed to differentiate between brain tumor recurrence and treatment-related changes following radiotherapy, based on conventional and advanced MRI methods. Vascular parameters extracted from dynamic contrast-enhanced (DCE) were suggested as potential markers for disease progression. Yet, most studies that proposed machine-learning methods or relayed on threshold values of DCE for classification, did not analyze separately Glioblastoma (GBM) and brain metastasis, and often offer the same method/threshold values, neglecting the different vascularity of these two tumor types. Understanding and quantifying the differences between tumors type can improve early diagnosis of recurrence and may improve the reliability of identifying treatment-related changes. The aim of this study was to quantify vascular parameters of tumor recurrence and to assess differences between GBM and brain metastasis, specifically metastasis of breast cancer. METHOD: 41 MRI scans were included: 24 from patients with GBM: 20 with recurrence and 4 with treatment-related changes, and 17 scans from patients with brain metastasis of breast cancer: 10 with recurrence and 7 with treatment-related changes (diagnosis were based on radiological follow-up/histopathology, when available). MRIs were performed on a 3.0 Tesla MRI scanner (Siemens MAGNETOM Prisma) and included T1, T1+contrast, and DCE images. DCE analysis was performed using DUSTER and Ktrans, Vp and Kep pharmacokinetic parameters were compared between groups. RESULTS: Patients with GBM often demonstrated both tumor recurrence and areas with treatment-related changes. Significantly differences were detected between tumor recurrence and treatment-related changes, and higher Vp and Ktrans were detected in recurrent GBM as compared to recurrent breast cancer metastasis. Cutoff for differentiation between tumor recurrence and treatment-related changes will be presented specifically for each tumor type. To conclude, when defining quantitative threshold values for clinical decision-making, the inherent differences between primary and metastatic brain tumors should be taken into account.