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. 2015 Jun;76(3):225-9.
doi: 10.1055/s-0034-1543965. Epub 2015 Jan 21.

Predicting Consistency of Meningioma by Magnetic Resonance Imaging

Affiliations

Predicting Consistency of Meningioma by Magnetic Resonance Imaging

Kyle A Smith et al. J Neurol Surg B Skull Base. 2015 Jun.

Abstract

Objective Meningioma consistency is important because it affects the difficulty of surgery. To predict preoperative consistency, several methods have been proposed; however, they lack objectivity and reproducibility. We propose a new method for prediction based on tumor to cerebellar peduncle T2-weighted imaging intensity (TCTI) ratios. Design The magnetic resonance (MR) images of 20 consecutive patients were evaluated preoperatively. An intraoperative consistency scale was applied to these lesions prospectively by the operating surgeon based on Cavitron Ultrasonic Surgical Aspirator (Valleylab, Boulder, Colorado, United States) intensity. Tumors were classified as A, very soft; B, soft/intermediate; or C, fibrous. Using T2-weighted MR sequence, the TCTI ratio was calculated. Tumor consistency grades and TCTI ratios were then correlated. Results Of the 20 tumors evaluated prospectively, 7 were classified as very soft, 9 as soft/intermediate, and 4 as fibrous. TCTI ratios for fibrous tumors were all ≤ 1; very soft tumors were ≥ 1.8, except for one outlier of 1.66; and soft/intermediate tumors were > 1 to < 1.8. Conclusion We propose a method using quantifiable region-of-interest TCTIs as a uniform and reproducible way to predict tumor consistency. The intraoperative consistency was graded in an objective and clinically significant way and could lead to more efficient tumor resection.

Keywords: Cavitron Ultrasonic Surgical Aspirator (CUSA); brain tumor; meningioma consistency; meningioma imaging; meningioma surgery.

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Conflict of interest statement

Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this article.

Figures

Fig. 1
Fig. 1
How to use the tumor to cerebellar peduncle T2-weighted imaging intensity (TCTI) ratio. (A) Region of interest (ROI) in tumor on T2 sequence. (B) ROI in middle cerebellar peduncle on T2 sequence. Calculating the TCTI ratio. TCTI ratio = intensity value for ROI within tumor (A)/Intensity value for ROI within middle cerebellar peduncle (B).
Fig. 2
Fig. 2
Tumor to cerebellar peduncle T2-weighted imaging intensity (TCTI) ratio example. (A) Region of interest (ROI) intensity within tumor = 976.6. (B) ROI intensity within middle cerebellar peduncle = 435.4. TCTI ratio = 976.6/435.4 = 2.243. Green circle indicates ROI chosen for calculation.
Fig. 3
Fig. 3
Tumor to cerebellar peduncle T2-weighted imaging intensity (TCTI) ratio example. (A) Region of interest (ROI) intensity within tumor = 341.3. (B) ROI intensity within cerebellar peduncle = 438.2. TCTI ratio = 341.3/438.2 = 0.779. Green circle indicates ROI chosen for calculation.

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