Differentiating Gliosarcoma from Glioblastoma: A Novel Approach Using PEACE and XGBoost to Deal with Datasets with Ultra-High Dimensional Confounders

Abstract

In this study, we used a recently developed causal methodology, called Probabilistic Easy Variational Causal Effect (PEACE), to distinguish gliosarcoma (GSM) from glioblastoma (GBM). Our approach uses a causal metric which combines Probabilistic Easy Variational Causal Effect (PEACE) with the XGBoost, or eXtreme Gradient Boosting, algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach uses the complete dataset with PEACE and the XGBoost algorithm. PEACE provides a comprehensive measurement of direct causal effects, applicable to both continuous and discrete variables. Our method provides both positive and negative versions of PEACE together with their averages to calculate the positive and negative causal effects of the radiomic features on the variable representing the type of tumor (GSM or GBM). In our model, PEACE and its variations are equipped with a degree d which varies from 0 to 1 and it reflects the importance of the rarity and frequency of the events. By using PEACE with XGBoost, we achieved a detailed and nuanced understanding of the causal relationships within the dataset features, facilitating accurate differentiation between GSM and GBM. To assess the XGBoost model, we used cross-validation and obtained a mean accuracy of 83% and an average model MSE of 0.130. This performance is notable given the high number of columns and low number of rows (code on GitHub).

Keywords

• Probabilistic Easy Variational Causal Effect (PEACE)
• causal inference
• XGBoost
• gliosarcoma
• glioblastoma