LAUSR

Predicting the survival risk of gliomas is vital for personalized treatment plans. The latest survival risk prediction methods primarily rely on histopathology and genomics, which are invasive and costly. However, predicting survival risk based on non‐invasive Magnetic Resonance Imaging (MRI) or handcrafted radiomics (HCRs) and clinical features has remained a challenge. The fusion of multi‐view, non‐invasive information holds the potential to improve risk prediction. Additionally, existing survival risk prediction methods typically depend on the Cox partial log‐likelihood loss as their main optimization criterion, which may overlook the survival rankings among gliomas, leading to discrepancies between risk prediction and actual outcomes.