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SPACT: A clustering-driven multi-modal framework for survival prediction using genomic and histopathology data
Fatma Ezgi Öğülmüş1, Shahaddin Gafarov2, Yasin Almalıoğlu3
1Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey.
Medical Image Analysis
|April 21, 2026
Summary
SPACT, a novel deep learning model, enhances cancer survival prediction by integrating multi-modal histopathology data. It achieves superior accuracy and robustness across diverse datasets, outperforming existing methods in most cancer types.
Area of Science:
- Oncology
- Computational Biology
- Artificial Intelligence
Background:
- Multi-modal data algorithms show promise in cancer prediction.
- Existing survival prediction models often rely on single datasets, limiting robustness.
Purpose of the Study:
- Introduce SPACT, a deep learning model for cancer survival probability prediction.
- Improve prediction accuracy and robustness using multi-modal histopathology data.
- Validate SPACT's performance on both TCGA and external datasets.
Main Methods:
- Developed SPACT, a deep learning model utilizing whole-slide histopathology (WSI) features.
- Integrated multi-modal data from TCGA and Başkent Hospital datasets.
- Performed cross-dataset encoder selection and ablation studies.
Main Results:
- SPACT achieved superior accuracy and robustness compared to state-of-the-art models in 5 of 7 cancer types.
- Demonstrated a c-index of 0.77 for ovarian cancer prediction.
- Encoders performing well on both datasets outperformed single-dataset encoders.
Conclusions:
- SPACT represents an improved multi-modal deep learning approach for cancer survival prediction.
- The model shows high individual and overall prediction accuracy and robustness.
- SPACT's findings support its clinical utility in diverse settings.
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