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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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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Updated: Dec 17, 2025

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Machine Learning Explainability in Breast Cancer Survival.

Tom Jansen1,2, Gijs Geleijnse2, Marissa Van Maaren2,3

  • 1Dept. of Computer Science, Vrije Universiteit Amsterdam, NL.

Studies in Health Technology and Informatics
|June 24, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict breast cancer survival, but their "black box" nature limits use. Explainability methods like LIME and SHAP help interpret predictions, aiding clinical acceptance.

Keywords:
Artificial Intelligenceinterpretabilityoncologyprediction model

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Area of Science:

  • Oncology
  • Computer Science
  • Medical Informatics

Background:

  • Machine learning (ML) offers potential in cancer diagnosis and treatment but faces adoption barriers due to low explainability.
  • The

Purpose of the Study:

  • To develop and interpret an ML model for predicting 10-year overall survival in breast cancer patients.
  • To evaluate the consistency and utility of LIME and SHAP explainability techniques in understanding ML predictions for breast cancer survival.

Main Methods:

  • Utilized data from the Netherlands Cancer Registry to build a ML model.
  • Applied Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to interpret model predictions.
  • Analyzed feature contributions and identified critical feature ranges where prediction outcomes shift.

Main Results:

  • The ML model successfully predicted 10-year overall survival for breast cancer patients.
  • LIME and SHAP demonstrated overall consistency in explaining feature contributions.
  • Identified specific feature ranges where LIME and SHAP exhibit mismatches, highlighting potential 'turning points' in survival prediction.

Conclusions:

  • Explainability techniques like LIME and SHAP can enhance the clinical acceptance of ML in oncology.
  • Further research is needed to evaluate and translate these explainability methods into real-world clinical applications.
  • Understanding feature-specific prediction shifts is crucial for advancing ML interpretability in cancer survival prediction.