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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.

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|April 21, 2026
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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.

Keywords:
CancerClusteringComputational pathologyFusionGenomicsMulti-modalSurvival

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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.