Diagnostic Value of Fractal Analysis for the Differentiation of Brain Tumors Using 3-Tesla Magnetic Resonance Susceptibility-Weighted Imaging

Antonio Di Ieva, Pierre Jean Le Reste*, Béatrice Carsin-Nicol, Jean Christophe Ferre, Michael D. Cusimano

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)839-846
    Number of pages7
    JournalNeurosurgery
    Volume79
    Issue number6
    DOIs
    Publication statusPublished - Dec 2016

    Keywords

    • Brain tumors
    • Fractal
    • MRI
    • Susceptibility-weighted imaging
    • SWI

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