Brain Tumor Characterization Using Multibiometric Evaluation of MRI

Abstract

The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically verified intracranial HGG (n = 18), LGG (n = 10), and MET (n = 15) were chosen. Preoperative magnetic resonance data included pre- and post-gadolinium contrast-enhanced T1-weighted fluid-attenuated inversion recover, cerebral blood flow (CBF), cerebral blood volume (CBV), fractional anisotropy, and apparent diffusion coefficient maps used for quantification of magnetic resonance biometrics by manual delineation of regions of interest. A binary logistic regression model was applied for multiparametric analysis and receiver operating characteristic (ROC) analysis. Statistically significant differences were found for normalized-ADC-tumor (nADC-T), normalized-CBF-tumor (nCBF-T), normalized-CBV-tumor (nCBV-T), and normalized-CBF-edema (nCBF-E) between LGG and HGG, and when these metrics were combined, HGG could be distinguished from LGG with a sensitivity and specificity of 100%. The only metric to distinguish HGG from MET was the normalized-ADC-E with a sensitivity of 68.8% and a specificity of 80%. LGG can be distinguished from MET by combining edema volume (Vol-E), Vol-E/tumor volume (Vol-T), nADC-T, nCBF-T, nCBV-T, and nADC-E with a sensitivity of 93.3% and a specificity of 100%. The present study confirms the usability of a multibiometric approach including volume, perfusion, and diffusion metrics in differentially diagnosing brain tumors in preoperative patients and adds to the growing body of evidence in the clinical field in need of validation and standardization.

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

“…As HGGs usually show more heterogeneous MRI characteristics, their automated segmentation could be expected to be more challenging compared to LGGs. Furthermore, the low proliferative state of LGGs likely results in lower perfusion and higher diffusion values in affected tissue [ 73 , 74 ]. No performance difference was observed between the segmentation of HGGs and LGGs.…”

Multivoxel 1H-MR Spectroscopy Biometrics for Preoprerative Differentiation between Brain Tumors

“…The differentiation between a small solid or ring-enhancing glioblastoma and a single metastasis is often more difficult (7). The imaging method of choice for evaluation of primary or secondary brain lesions is magnetic resonance imaging (MRI) using conventional magnetic resonance (MR) sequences, as well as more advanced imaging techniques like perfusion, diffusion, and magnetic resonance spectroscopy (MRS) (8).…”

Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis

“…Since most methods used conventional MRI scans (ie, T1, T2, T1CE, and FLAIR), future studies could combine these multimodal sequences with other specialized MRI sequences to increase the number of features, assessing for potential enhanced segmentation results. Soltaninejad et al 30 and Durmo et al 98 incorporated features obtained from diffusion-weighted and diffusion tensor imaging and showed promising results in the automated identification of brain tumors. Including other MRI sequences in publicly available datasets, such as BRATS, could facilitate investigations into the diagnostic value of additional features.…”

“…This may be due to LGG’s slow growth, lack of surrounding vasogenic edema, and poor enhancement on MRI, making LGGs radiologically more difficult to identify. 98 Moreover, HGGs are highly proliferative tumors resulting in higher lesion contrast and enhancement, making them radiologically more noticeable. 98 This study shows that although manual WT segmentation statistically outperformed automated segmentation for HGG, both achieved “good” performance (DSC ≥ 0.7).…”