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Related Experiment Video

Updated: Aug 20, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Colorectal Cancer Survival Prediction Using Deep Distribution Based Multiple-Instance Learning.

Xingyu Li1, Jitendra Jonnagaddala2, Min Cen1

  • 1School of Management, University of Science and Technology of China, Hefei 230026, China.

Entropy (Basel, Switzerland)
|November 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces DeepDisMISL, a novel algorithm for colorectal cancer (CRC) survival prediction using whole slide images. It improves accuracy by analyzing holistic patch information, outperforming existing methods and aiding clinical decisions.

Keywords:
deep learningmultiple instance learningsurvival analysiswhole slide images

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Area of Science:

  • Digital pathology
  • Computational oncology
  • Machine learning in cancer research

Background:

  • Current deep learning models for cancer survival prediction using whole slide images (WSIs) often focus on limited image patches (highest or highest/lowest scores).
  • This approach may not capture the full spectrum of morphological information crucial for accurate survival prediction.

Purpose of the Study:

  • To develop and validate a novel distribution-based multiple-instance survival learning algorithm (DeepDisMISL) for colorectal cancer (CRC) survival prediction.
  • To investigate whether incorporating holistic patch information, beyond just highest/lowest scores, enhances predictive accuracy.

Main Methods:

  • Developed DeepDisMISL, a distribution-based multiple-instance survival learning algorithm.
  • Validated DeepDisMISL on two large international CRC WSIs datasets (MCO CRC and TCGA COAD-READ).
  • Compared DeepDisMISL's performance against state-of-the-art algorithms.

Main Results:

  • Combining patches scored by percentile distributions with highest and lowest scored patches significantly improved CRC survival prediction performance.
  • Incorporating neighborhood instances around selected distribution locations further enhanced predictive capabilities.
  • DeepDisMISL demonstrated superior predictive ability compared to existing state-of-the-art algorithms.

Conclusions:

  • Holistic patch information analysis, particularly using percentile distributions, is more effective for CRC survival prediction than traditional patch selection methods.
  • DeepDisMISL offers a powerful, interpretable tool for predicting cancer survival and understanding the link between morphology and patient outcomes.
  • The algorithm can assist clinicians in risk stratification and personalized treatment strategies for colorectal cancer.