Evaluating Sociodemographic Biases in Artificial Intelligence-Based Glioblastoma Response Assessment Algorithms

Demographic bias was relatively low in glioblastoma MRI segmentation models. The model trained on the smallest and most homogenous dataset exhibited the most bias. Greater demographic heterogeneity even without increasing training dataset size was associated with reduced bias. The BraTS model, trained on a moderate-sized cohort that included more diverse tumor types, performed better and demonstrated less bias than the FeTS2 model, despite the FeTS2 being trained on the largest dataset.