Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy
Researchers have developed a new framework called Multimodality Stacking with Blockwise missing values (MSB) to address challenges in integrating multimodal datasets for clinical oncology. MSB is a late-fusion framework designed for survival analysis that can handle situations where entire data sources are unavailable for certain patient subsets. When applied to the PIONeeR study to predict progression-free survival in lung cancer patients, MSB demonstrated improved predictive performance over existing algorithms, significantly reducing the generalization gap and identifying key predictive biomarkers. AI