What question did this study set out to answer?

To propose a self-supervised learning framework for survival prediction and prognostic stratification in glioblastoma patients.

November 1, 2025Neuro-Oncology

IMG-66. Self-supervised multimodal learning for survival prediction in glioblastoma: a multicenter study from the ReSPOND consortium

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Authors

FYFanyang Yu JGJun Guo YTYu Tian

Discussion

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Overview

Multicenter study demonstrates improved survival prediction in glioblastoma using multimodal data integration, highlighting the role of clinical factors.

Key Points

To propose a self-supervised learning framework for survival prediction and prognostic stratification in glioblastoma patients.
Curated multi-parametric MRI dataset of 3,119 glioblastoma patients from 22 institutions.
Adapted masked autoencoder for training Vision Transformer encoder on imaging data.
Incorporated clinical information through cross-attention mechanism for feature aggregation.
Used multi-layer perceptron for log-risk estimation, optimized with Cox partial likelihood.
Evaluated model performance through k-fold cross-validation and leave-one-site-out validation.
Achieved highest C-index of 0.674 ± 0.017 on the ReSPOND consortium.
Integration of clinical information led to improved model performance across sites.
Kaplan-Meier analysis indicated more distinct prognostic subgroups.

Cite This Study

Yu et al. (2025) studied this question.

synapsesocial.com/papers/69254362c0ce034ddc35837d https://doi.org/10.1093/neuonc/noaf201.1145

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November 1, 2025Neuro-Oncology

IMG-66. Self-supervised multimodal learning for survival prediction in glioblastoma: a multicenter study from the ReSPOND consortium

View Full Paper

Ask AI

Bookmark

Authors

FYFanyang Yu JGJun Guo YTYu Tian

Discussion

Member takes

Overview

Multicenter study demonstrates improved survival prediction in glioblastoma using multimodal data integration, highlighting the role of clinical factors.

Key Points

To propose a self-supervised learning framework for survival prediction and prognostic stratification in glioblastoma patients.
Curated multi-parametric MRI dataset of 3,119 glioblastoma patients from 22 institutions.
Adapted masked autoencoder for training Vision Transformer encoder on imaging data.
Incorporated clinical information through cross-attention mechanism for feature aggregation.
Used multi-layer perceptron for log-risk estimation, optimized with Cox partial likelihood.
Evaluated model performance through k-fold cross-validation and leave-one-site-out validation.
Achieved highest C-index of 0.674 ± 0.017 on the ReSPOND consortium.
Integration of clinical information led to improved model performance across sites.
Kaplan-Meier analysis indicated more distinct prognostic subgroups.

Cite This Study

Yu et al. (2025) studied this question.

synapsesocial.com/papers/69254362c0ce034ddc35837d https://doi.org/10.1093/neuonc/noaf201.1145

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