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Flexible Risk Evidence Combination Rules in Breast Cancer Precision Therapy.

Michael Kenn1,2, Rudolf Karch1,3, Christian F Singer4

  • 1Section of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.

Journal of Personalized Medicine
|January 21, 2023
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Summary
This summary is machine-generated.

This study presents new evidence combination rules for Dempster-Shafer theory, improving hormone receptor status determination in breast cancer. The methods offer flexible fusion strategies for diverse data, enhancing diagnostic accuracy.

Keywords:
Dempster-Shafer theorybreast cancerdata scienceevidence theoryhormone receptor statusmathematical oncologypersonalized medicineprecision medicinetheory of belief functions

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Oncology

Background:

  • Dempster-Shafer theory offers a framework for combining evidence from multiple sources.
  • Accurate hormone receptor status determination is crucial for breast cancer treatment decisions.
  • Previous work introduced Dempster-Shafer theory for breast cancer hormone receptor analysis.

Purpose of the Study:

  • To address the challenge of selecting appropriate evidence fusion methods in Dempster-Shafer theory.
  • To develop a parameterized family of evidence combination rules for diverse data types and qualities.
  • To model the impact of different decision strategies on real patient samples.

Main Methods:

  • Developed a parameterized family of evidence combination rules.
  • Investigated rules that range from emphasizing discrepancies to aspiring accordance.
  • Modeled the consequences of different decision strategies using real patient data.

Main Results:

  • The proposed rules offer flexibility in fusing evidence based on data characteristics.
  • Different fusion strategies can lead to varied outcomes in hormone receptor status determination.
  • The study demonstrates the practical implications for breast cancer diagnostics.

Conclusions:

  • The developed evidence combination rules enhance the adaptability of Dempster-Shafer theory for breast cancer diagnostics.
  • Selecting appropriate fusion strategies is critical for accurate hormone receptor status determination.
  • This work provides a valuable tool for improving breast cancer patient sample analysis.