Introduction: Meningioma consistency is an important characteristic as it affects the difficulty of surgery. Regarding preoperative prediction of 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 and introduce an objective, intraoperative grading method.

Methods: Twenty consecutive patients who underwent resection of meningioma were examined. An intraoperative consistency scale was applied to these lesions prospectively by the operating surgeon based on Cavitron ultrasonic aspirator intensity used. Tumors were classified as A, very soft; B, soft/intermediate; or C, fibrous. Using T2-weighted MR sequence, regions of interest were chosen from the lesion and from the cerebellar peduncle, and 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.

Conclusions: Predicting the consistency of meningioma can significantly affect the decision-making of the operating surgeon. We propose a method using quantifiable ROI intensity ratios of tumor in comparison to the cerebellar peduncle, as a uniform and reproducible way to predict tumor consistency. Additionally, the intraoperative consistency was graded in an objective and clinically significant way.

Patient Care: Predicting the consistency of meningioma can affect the decision-making of the surgeon. We propose a method of tumor consistency prediction which may better assist surgeons with preoperative planning for tumor resection.

Learning Objectives: By the conclusion of this session, participants should be able to: 1) Understand the importance of meningioma consistency and its impact on surgical difficulty, 2) Describe current methods of preoperative tumor consistency prediction, and 3) Understand our proposed tumor consistency grading scale and method of T2-weighted imaging prediction.

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