Quantifying the Growth of Glioblastoma Tumors Using Multimodal MRI Brain Images

Simple Summary Prediction of volume expected to be attained by a tumor of fourth grade malignancy becomes difficult when problem is subject to changes in time or when there exists heterogeneity among oncogenes for different subjects. The attempt here was to develop a time independent model which will only depend on some other radiomic properties of the tumor, also accommodating for the heterogeneity feature. Our model gives highly accurate results when we subject the initial volume of the tumor and eventually discover that the probability of no tumor cells remaining undetected is sufficiently high. Our model results are for glioblastoma tumor, but it can be applied on any other tumor volume prediction problem, and especially can be reliably adopted for tumors of high malignancy level. Predicting the eventual volume of tumor cells, that might proliferate from a given tumor, can help in cancer early detection and medical procedure planning to prevent their migration to other organs. In this work, a new statistical framework is proposed using Bayesian techniques for detecting the eventual volume of cells expected to proliferate from a glioblastoma (GBM) tumor. Specifically, the tumor region was first extracted using a parallel image segmentation algorithm. Once the tumor region was determined, we were interested in the number of cells that could proliferate from this tumor until its survival time. For this, we constructed the posterior distribution of the tumor cell numbers based on the proposed likelihood function and a certain prior volume. Furthermore, we extended the detection model and conducted a Bayesian regression analysis by incorporating radiomic features to discover those non-tumor cells that remained undetected. The main focus of the study was to develop a time-independent prediction model that could reliably predict the ultimate volume a malignant tumor of the fourth-grade severity could attain and which could also determine if the incorporation of the radiomic properties of the tumor enhanced the chances of no malignant cells remaining undetected.

Automated summary

This study proposed a time-independent model to predict the ultimate volume of a high malignancy tumor and determine if the radiomic properties of the tumor enhanced the chances of no malignant cells remaining undetected.