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Cancer Survival Analysis01:21

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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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Overall mortality risk analysis for rectal cancer using deep learning-based fuzzy systems.

Cheng-Hong Yang1, Wen-Ching Chen2, Jin-Bor Chen3

  • 1Department of Information Management, Tainan University of Technology, Tainan, Taiwan; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan; Ph. D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung, Taiwan; School of Dentistry, Kaohsiung Medical University, Kaohsiung, Taiwan; Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, Taiwan.

Computers in Biology and Medicine
|March 25, 2023
PubMed
Summary

A novel Fuzzy-based RNNCoxPH model integrates fuzzy logic, recurrent neural networks (RNNs), and Cox proportional hazards regression (CoxPH) to accurately detect missense variants linked to colorectal cancer mortality. This advanced approach significantly improves risk prediction for better cancer treatment guidance.

Keywords:
Cancer mortalityCoxPHDeep learningFuzzy logicRectal cancer

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Colorectal cancer is a major global health concern with rising incidence.
  • Genomic data integration offers promise for personalized cancer therapy but faces analytical challenges.
  • High-throughput genomic data analysis requires advanced computational methods for clinical translation.

Purpose of the Study:

  • To develop an advanced analytic approach for identifying missense variants associated with mortality in rectum adenocarcinoma.
  • To integrate fuzzy logic, recurrent neural networks (RNNs), and Cox proportional hazards regression (CoxPH) into a novel model.
  • To improve the accuracy of predicting cancer mortality risk based on genetic variations.

Main Methods:

  • Utilized the TCGA-Read dataset from the Genomic Data Commons.
  • Developed four risk score models: RNN, CoxPH, RNNCoxPHAddition, and RNNCoxPHMultiplication.
  • Employed fuzzy logic to calculate survival risk values and classify variant membership grades for enhanced identification.

Main Results:

  • Identified 20,028 variants associated with survival status, including 2,390 linked to mortality.
  • The proposed Fuzzy-based RNNCoxPH model achieved a balanced accuracy of 93.7%.
  • Outperformed conventional methods like CoxPH and machine learning models such as XGBoost in accuracy.

Conclusions:

  • The Fuzzy-based RNNCoxPH model demonstrates superior efficacy in identifying and classifying missense variants related to mortality risk in rectum adenocarcinoma.
  • This approach offers a significant advancement in translational oncology for precision cancer treatment.
  • Highlights the potential of integrating diverse computational techniques for complex genomic data analysis in cancer research.