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Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
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Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based

Li-Yeh Chuang1, Guang-Yu Chen2, Sin-Hua Moi2

  • 1Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan.

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Summary

Biogeography-based optimization (BBO) identified key breast cancer factors for survival prediction. The BBO-selected model achieved 80% accuracy, aiding in patient follow-up and management.

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Area of Science:

  • Oncology
  • Biostatistics
  • Computational Intelligence

Background:

  • Breast cancer is a leading global health concern for women.
  • Accurate prognosis prediction is crucial for effective patient management.
  • Novel computational methods can enhance the analysis of clinicopathologic data.

Purpose of the Study:

  • To apply the Biogeography-based optimization (BBO) algorithm for selecting significant clinicopathologic variables in breast cancer.
  • To develop a Cox proportional hazard (PH) regression model using BBO-selected variables to predict overall survival.
  • To evaluate the predictive accuracy of the developed survival model.

Main Methods:

  • Utilized a dataset of 1896 breast cancer patients from 2005-2017.
  • Employed the Biogeography-based optimization (BBO) algorithm to identify predictive clinicopathologic variables.
  • Applied Cox PH regression and C-statistics to assess survival prediction accuracy.

Main Results:

  • The BBO-selected model achieved the highest C-statistic value of 80% for predicting overall survival.
  • Key predictors included tumor size, lymph node metastasis, lymphovascular invasion, dermal invasion, total mastectomy, and absence of hormone therapy.
  • This model identified the minimum necessary variables for optimal discrimination.

Conclusions:

  • The BBO algorithm effectively selects crucial variables for breast cancer survival prediction.
  • The developed Cox PH model demonstrates strong predictive ability for patient outcomes.
  • This approach can significantly contribute to breast cancer follow-up and clinical decision-making.