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New MSB Framework Improves Survival Prediction in Oncology

By PulseAugur Editorial · [2 sources] ·

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

RANK_REASON The cluster contains an academic paper detailing a new statistical framework for survival analysis in oncology.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New MSB Framework Improves Survival Prediction in Oncology

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mohamed Boussena, Florence Monville, Jacques Fieschi-Meric, Frederic Vely, Pierre Milpied, Julien Mazieres, Maurice Perol, Eric Vivier, Laurent Greillier, Fabrice Barlesi, Sebastien Benzekry ·

    Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy

    arXiv:2605.25050v1 Announce Type: cross Abstract: Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets. Standard survival models often s…

  2. arXiv stat.ML TIER_1 English(EN) · Sebastien Benzekry ·

    Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy

    Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets. Standard survival models often struggle with these gaps, leading to biased results…

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