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Related Experiment Video

Updated: Jun 19, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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An Interpretable Omics-to-Image Transformer Framework for Cancer Prognosis Prediction.

Yanping Jiang1, Wenhao Sun2, Tianjun Lan2,3

  • 1School of Mathematics, Foshan University, Foshan 528000, China.

Computational and Structural Biotechnology Journal
|April 27, 2026
PubMed
Summary

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Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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This summary is machine-generated.

This study introduces an adaptive Omics-to-Image Transformer framework for Cancer prognosis Evaluation (OTCE) to improve multi-omics cancer survival prediction. OTCE enhances accuracy and interpretability, identifying key prognostic biomarkers.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Cancer Research

Background:

  • Accurate cancer prognosis is crucial for personalized treatment and clinical decisions.
  • Multi-omics data (mRNA, microRNA, DNA methylation) offer complementary insights but pose challenges like high dimensionality and complex dependencies for survival models.
  • Existing models struggle with heterogeneity, interpretability, and cross-modal learning from diverse omics data.

Purpose of the Study:

  • To develop an advanced framework for accurate and interpretable multi-omics cancer prognosis prediction.
  • To address the challenges of high dimensionality, heterogeneity, and cross-modal dependencies in multi-omics survival analysis.
  • To identify novel prognostic biomarkers for various cancer types.

Main Methods:

Related Experiment Videos

Last Updated: Jun 19, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.0K
  • Proposed an adaptive Omics-to-Image Transformer framework for Cancer prognosis Evaluation (OTCE).
  • Converted heterogeneous multi-omics data into unified pseudo-image and multichannel image representations for cross-modal learning.
  • Employed a parallel multiview deep neural network to capture global, local, and long-range cross-modal dependencies.

    • Main Results:

    • OTCE outperformed state-of-the-art survival models by an average of 7.7% in concordance index (C-index) across 6 cancer datasets.
    • Identified 7 prognostic candidate biomarkers in kidney renal clear cell carcinoma using integrated feature attribution and differential expression analysis.
    • Validated biological relevance of identified biomarkers through single-cell and spatial transcriptomic analyses.

    Conclusions:

    • OTCE significantly improves the accuracy and robustness of multi-omics cancer prognosis prediction.
    • The framework enhances model interpretability and provides a scalable solution for integrative survival analysis.
    • OTCE offers valuable insights for prognostic biomarker discovery across multiple cancer types.