BACKGROUND: High-field, intraoperative magnetic resonance imaging (iMRI) achieves free tumor margins in glioma surgery by involving anatomic neuronavigation and sophisticated functional imaging.
OBJECTIVE: To evaluate the role of perfusion-weighted iMRI as an aid to detect residual tumor and to guide its resection.
METHODS: Twenty-two patients undergoing intraoperative scanning (in a dual-room 1.5-T magnet setting) during the resection of high-grade gliomas were examined with perfusion-weighted iMRI. The generated relative cerebral blood volume (rCBV) maps were scrutinized for any hot spots indicative of tumor remnants, and region-of-interest analysis was performed. Differences among the rCBV region-of-interest estimates in residual tumor, free tumor margins, and normal white matter were analyzed. Histopathology of the tissue specimens and the neurosurgeon’s intraoperative macroscopic estimations were considered the reference standards.
RESULTS: In all cases, diagnostic rCBV perfusion maps were generated. Interpretation of perfusion maps demonstrated that gross total resection of gliomas was achieved in 4 of 22 cases (18%), which was macroscopically and histopathologically verified, whereas in 18 of 22 cases (82%), the perfusion-weighted iMRI revealed hot spots indicating subtotal tumor removal. The latter proved to be true in all but 1 case. The receiver-operating characteristic curves of the qualitative visual and quantitative analyses showed excellent sensitivity and specificity rates. Statistical analysis demonstrated statistically significant differences for the mean rCBV and maximum rCBV between residual disease and tumor-free margins (P = .002 for both).
CONCLUSION: Perfusion-weighted iMRI may be implemented easily into imaging protocols and may assist the surgeon in detecting residual tumor volume.
ABBREVIATIONS: CBV, cerebral blood volume
CI, confidence interval
iMRI, intraoperative magnetic resonance imaging
rCBV, relative cerebral blood volume
ROC, receiver-operating characteristic
WHO, World Health Organization