3D Virtual Modeling for Morphological Characterization of Pituitary Tumors: Preliminary Results on Its Predictive Role in Tumor Resection Rate

Abstract

Among potential factors affecting the surgical resection in pituitary tumors, the role of tumor three-dimensional (3D) features is still unexplored. The aim of this study is to introduce the use of 3D virtual modeling for geometrical and morphological characterization of pituitary tumors and to evaluate its role as a predictor of total tumor removal. A total of 75 patients operated for a pituitary tumor have been retrospectively reviewed. Starting from patient imaging, a 3D tumor model was reconstructed, and 3D characterization based on tumor volume (Vol), area, sphericity (Spher), and convexity (Conv) was provided. The extent of tumor removal was then evaluated at post-operative imaging. Mean values were obtained for Vol (9117 ± 8423 mm3), area (2352 ± 1571 mm2), Spher (0.86 ± 0.08), and Conv (0.88 ± 0.08). Total tumor removal was achieved in 57 (75%) cases. The standard prognostic Knosp grade, Vol, and Conv were found to be independent factors, significantly predicting the extent of tumor removal. Total tumor resection correlated with lower Knosp grades (p = 0.032) and smaller Vol (p = 0.015). Conversely, tumors with a more irregular shape (low Conv) have an increased chance of incomplete tumor removal (p = 0.022). 3D geometrical and morphological features represent significant independent prognostic factors for pituitary tumor resection, and they should be considered in pre-operative planning to allow a more accurate decision-making process.