The role of 3-Tesla magnetic resonance perfusion and spectroscopy in distinguishing glioblastoma from solitary brain metastasis

Abstract

Objectives: This study aimed to assess the value of magnetic resonance perfusion (MR perfusion) and magnetic resonance spectroscopy (MR spectroscopy) in 3.0-Tesla magnetic resonanceimaging (MRI) for differential diagnosis of glioblastoma (GBM) and solitary brain metastasis (SBM). Material and Methods: This retrospective study involved 36 patients, including 24 cases of GBM and 12 of SBM diagnosed using histopathology. All patients underwent a 3.0-Tesla MRI examination with pre-operative MR perfusion and MR spectroscopy. We assessed the differences in age, sex, cerebral blood volume (CBV), relative CBV (rCBV), and the metabolite ratios of choline/N-acetylaspartate (Cho/NAA) and Cho/creatine between the GBM and SBM groups using the Mann–Whitney U-test and Chi-square test. The cutoff value, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value of the significantly different parameters between these two groups were determined using the receiver operating characteristic curve. Results: In MR perfusion, the CBV of the peritumoral region (pCBV) had the highest preoperative predictive value in discriminating GBM from SBM (cutoff: 1.41; sensitivity: 70.83%; and specificity: 83.33%), followed by the ratio of CBV of the solid tumor component to CBV of normal white matter (rCBVt/n) and the ratio of CBV of the pCBV to CBV of normal white matter (rCBVp/n). In MR spectroscopy, the Cho/NAA ratio of the pCBV (pCho/NAA; cutoff: 1.02; sensitivity: 87.50%; and specificity: 75%) and the Cho/NAA ratio of the solid tumor component (tCho/NAA; cutoff: 2.11; sensitivity: 87.50%; and specificity: 66.67%) were significantly different between groups. Moreover, combining these remarkably different parameters increased their diagnostic utility for distinguishing between GBM and SBM. Conclusion: pCBV, rCBVt/n, rCBVp/n, pCho/NAA, and tCho/NAA are useful indices for differentiating between GBM and SBM. Combining these indices can improve diagnostic performance in distinguishing between these two tumors.

Aided Diagnosis Model Based on Deep Learning for Glioblastoma, Solitary Brain Metastases, and Primary Central Nervous System Lymphoma with Multi-Modal MRI

“…Glioblastoma (GBM) is a malignant brain tumor formed as a result of mutations in the genetic material and epigenetic mechanisms driving continuous cell cycle progression and mitosis in brain cells, leading to abnormal energy metabolism that supports sustained growth [1]. Solitary brain metastases (SBM), on the other hand, refer to the highly malignant tumors that have spread to distant organs or tissues through the bloodstream [2]. Primary central nervous system lymphoma (PCNSL) is a rare and highly malignant non-Hodgkin lymphoma characterized by the malignant clonal proliferation of lymphocytes, including intracerebral lymphocytes and lymphocytes with central nervous system involvement [3].…”

Innovative studies in the Journal of Clinical Imaging Science

“…Articles from Vietnam discussed the comparison of three-dimensional (3D) T1-weighted (T1W) gradient-echo (GRE) and 2D T1W in-phase and out-of-phase GRE sequences for appendicitis diagnosis in pregnant women [ 4 ] as well as the role of 3-Tesla magnetic resonance perfusion and spectroscopy for differential diagnosis of glioblastoma and solitary brain metastasis. [ 5 ] Authors from Denmark brought to our readers a study aimed at prospectively validating the magnetic resonance imaging algorithm presented by Cornelis et al . for renal cell carcinoma classification.…”