Predicting tumor consistency and extent of resection in non-functioning pituitary tumors

Abstract

Purpose: To (1) identify a radiological parameter to predict non-functioning pituitary tumor (NFPT) consistency, (2) examine the relationship between NFPT consistency and extent of resection (EOR), (3) investigate if tumor consistency predictors can anticipate EOR.

Methods: The ratio (T2SIR) between the T2 min signal intensity (SI) of the tumor and the T2 mean SI of the CSF was the main radiological parameter, being determined through a radiomic-voxel analysis and calculated using the following formula: T2SIR = [(T2 tumor mean SI - SD)/T2 CSF SI]. The tumor consistency was pathologically estimated as collagen percentage (CP). EOR of NFPTs was evaluated by exploiting a volumetric technique and its relationship with the following explanatory variables was explored: CP, Knosp-grade, tumor volume, inter-carotid distance, sphenoidal sinus morphology, Hardy-grade, suprasellar tumor extension.

Results: A statistically significant inverse correlation between T2SIR and CP was demonstrated (p = 0.0001), with high diagnostic power of T2SIR in predicting NFPT consistency (ROC curve analysis' AUC = 0.88; p = 0.0001). The following predictors of EOR were identified in the univariate analysis: CP (p = 0.007), preoperative volume (p = 0.045), Knosp grade (p = 0.0001), tumor suprasellar extension (p = 0.044). The multivariate analysis demonstrated two variables as unique predictors of EOR: CP (p = 0.002) and Knosp grade (p = 0.001). The T2SIR was a significant predictor of EOR both in the univariate (p = 0.01) and multivariate model (p = 0.003).

Conclusion: This study offers the potential to improve NFPT preoperative surgical planning and patient counseling by employing the T2SIR as a preoperative predictor of tumor consistency and EOR. Meanwhile, tumor consistency and Knosp grade were found to play an important role in predicting EOR.

Keywords: Consistency; EOR; Extent of resection; Pituitary adenomas; Pituitary tumors; Transsphenoidal.