Background: Susceptibility-weighted imaging (SWI) of brain tumors provides information about neoplastic vasculature and intratumoral micro- and macrobleedings. Low- and high-grade gliomas can be distinguished by SWI due to their different vascular characteristics. Fractal analysis allows for quantification of these radiological differences by a computer-based morphological assessment of SWI patterns.
Objective: To show the feasibility of SWI analysis on 3-T magnetic resonance imaging to distinguish different kinds of brain tumors.
Methods: Seventy-eight patients affected by brain tumors of different histopathology (low- and high-grade gliomas, metastases, meningiomas, lymphomas) were included. All patients underwent preoperative 3-T magnetic resonance imaging including SWI, on which the lesions were contoured. The images underwent automated computation, extracting 2 quantitative parameters: the volume fraction of SWI signals within the tumors (signal ratio) and the morphological self-similar features (fractal dimension [FD]). The results were then correlated with each histopathological type of tumor.
Results: Signal ratio and FD were able to differentiate low-grade gliomas from grade III and IV gliomas, metastases, and meningiomas (P < .05). FD was statistically different between lymphomas and high-grade gliomas (P < .05). A receiver-operating characteristic analysis showed that the optimal cutoff value for differentiating low- from high-grade gliomas was 1.75 for FD (sensitivity, 81%; specificity, 89%) and 0.03 for signal ratio (sensitivity, 80%; specificity, 86%).
Conclusion: FD of SWI on 3-T magnetic resonance imaging is a novel image biomarker for glioma grading and brain tumor characterization. Computational models offer promising results that may improve diagnosis and open perspectives in the radiological assessment of brain tumors.
Abbreviations: FD, fractal dimensionSR, signal ratioSWI, susceptibility-weighted imaging.