Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis

• Published in: Journal of magnetic resonance imaging Vol. 50; no. 2; pp. 519 - 528
• Main Authors: Artzi, Moran, Bressler, Idan, Ben Bashat, Dafna
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.08.2019
• Subjects: Accuracy; Algorithms; Brain; Brain cancer; Brain tumors; Breast; Classification; Classifiers; Data acquisition; Datasets; Decision trees; Differentiation; Feature extraction; Field strength; Glioblastoma; Image acquisition; Image contrast; Image processing; Image segmentation; Learning algorithms; Lungs; Machine learning; Magnetic resonance imaging; Medical imaging; Medical treatment; Metastases; Metastasis; Neuroimaging; Optimization; Patients; Radiomics; Sensitivity; Statistical analysis; Statistical tests; Support vector machines; Tumors; Wavelet analysis
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.26643

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.