Shape and Intensity Analysis of Glioblastoma Multiforme Tumors

Yi Tang Chen, Sebastian Kurtek; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 553-560

We use a geometric approach to characterize tumor shape and intensity along the tumor contour in the context of Glioblastoma Multiforme. Properties of the proposed shape+intensity representation include invariance to translation, scale, rotation and reparameterization, which allow for objective comparison of tumor features. Controlling for the weight of intensity information in the shape+intensity representation results in improved comparisons between tumor features of different patients who have been diagnosed with Glioblastoma Multiforme; further, it allows for identification of different partitions of the data associated with different median survival among such patients. Our findings suggest that integrating and appropriately balancing information regarding GBM tumor shape and intensity can be beneficial for disease prognosis. We evaluate the proposed statistical framework using simulated examples as well as a real dataset of Glioblastoma Multiforme tumors.