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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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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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Tumor Progression02:07

Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Deep Neural Networks Integrating Genomics And Histopathological Images For Predicting Stages And Survival Time-to-event In Colon Cancer.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Deep Neural Networks Integrating Genomics And Histopathological Images For Predicting Stages And Survival Time-to-event In Colon Cancer.

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Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer.

Olalekan Ogundipe1, Zeyneb Kurt2, Wai Lok Woo1

  • 1Department of Computer and Information Sciences, University of Northumbria, Newcastle Upon Tyne, United Kingdom.

Plos One
|September 3, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Integrating histopathology images with genomics data significantly improves colon cancer staging and risk stratification. This combined approach enhances classification accuracy and identifies key survival predictors, leading to better patient outcomes.

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Area of Science:

  • Oncology
  • Computational Biology
  • Bioinformatics

Background:

  • Existing colon cancer staging lacks precision due to variations in genomics and histopathology data alone.
  • Improved staging is crucial for optimizing treatment strategies and patient outcomes.

Purpose of the Study:

  • To develop an integrative deep neural network model for colon cancer staging.
  • To combine histopathological atypia patterns with multi-omics data (mRNA, miRNA, DNA methylation) for enhanced classification.
  • To stratify patients into low or high-risk survival groups.

Main Methods:

  • Utilized Deep Neural Networks (DNNs) to integrate diverse data sources.
  • Aggregated histopathological image features with genomics data (mRNA, miRNA, DNA methylation).
  • Classified colon cancer stages and stratified patient survival risk.

    • Main Results:

    • The integrated model achieved an Area Under Curve-Receiver Operating Characteristic (AUC-ROC) of 0.97 for stage classification, outperforming genomics-only (0.78) and image-only approaches.
    • A slight accuracy improvement of 0.08% was observed with integrated features compared to genomics-only.
    • 68% of the 2,700 fused features showed statistically significant differences in survival probability between low and high-risk groups.

    Conclusions:

    • Integrative analysis of histopathology and multi-omics data significantly enhances colon cancer staging and risk prediction.
    • The developed DNN model effectively stratifies patients into distinct survival risk groups.
    • This approach holds promise for personalized treatment strategies in colon cancer.