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Analyzing breast cancer invasive disease event classification through explainable artificial intelligence.

Raffaella Massafra1, Annarita Fanizzi1, Nicola Amoroso2,3

  • 1IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy.

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|February 23, 2023
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Summary

Explainable AI identifies key breast cancer invasive disease event (IDE) predictors. Features like age and tumor size are crucial in the 5-year outlook, while therapy details and lymphovascular invasion are vital for 10-year predictions.

Keywords:
10-year follow up5-year follow upbreast cancerexplainable AIinvasive disease events

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Machine learning and deep learning models are increasingly used to predict breast cancer invasive disease events (IDEs).
  • However, these "black box" models lack interpretability, hindering clinical adoption.
  • Explainable Artificial Intelligence (XAI) offers a solution to understand the factors driving AI predictions.

Purpose of the Study:

  • To develop and apply an XAI framework to identify key predictors of breast cancer IDEs.
  • To analyze these predictors over different timeframes relevant to clinical practice (5 and 10 years).
  • To bridge the gap between advanced AI techniques and their practical application in oncology.

Main Methods:

  • An XAI framework utilizing Shapley values was developed.
  • The framework was applied to a cohort of 486 breast cancer patients.
  • Features driving IDEs were identified for 5-year and 10-year prediction windows.

Main Results:

  • Within 5 years, predominant IDE predictors include age, tumor diameter, surgery type, and multiplicity.
  • Over 10 years, therapy-related features (hormone, chemotherapy) and lymphovascular invasion become dominant.
  • Estrogen Receptor (ER) status, Ki67 proliferation index, and metastatic lymph nodes are significant predictors across both timeframes.

Conclusions:

  • The XAI framework successfully identified distinct sets of features influencing breast cancer IDEs at 5 and 10 years.
  • This approach enhances the interpretability of AI models in breast cancer prognostics.
  • The findings can inform clinical decision-making and personalize patient follow-up strategies.