Generating Counterfactual Explanations For Causal Inference in Breast Cancer Treatment Response

Siqiong Zhou, Nicholaus Pfeiffer, Upala J. Islam, Imon Banerjee, Bhavika K. Patel, Ashif S. Iquebal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Imaging phenotypes extracted via radiomics of magnetic resonance imaging has shown great potential at predicting the treatment response in breast cancer patients after administering neoadjuvant systemic therapy (NST). Existing machine learning models are, however, limited in providing an expert-level interpretation of these models, particularly interpretability towards generating causal inference. Causal relationships between imaging phenotypes, clinical information, molecular features, and the treatment response may be useful in guiding the treatment strategies, management plans, and gaining acceptance in medical communities. In this work, we leverage the concept of counterfactual explanations to extract causal relationships between various imaging phenotypes, clinical information, molecular features, and the treatment response after NST. We implement the methodology on a publicly available breast cancer dataset and demonstrate the causal relationships generated from counterfactual explanations. We also compare and contrast our results with traditional explanations, such as LIME and Shapley.

Original languageEnglish (US)
Title of host publication2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022
PublisherIEEE Computer Society
Pages955-960
Number of pages6
ISBN (Electronic)9781665490429
DOIs
StatePublished - 2022
Externally publishedYes
Event18th IEEE International Conference on Automation Science and Engineering, CASE 2022 - Mexico City, Mexico
Duration: Aug 20 2022Aug 24 2022

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2022-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Country/TerritoryMexico
CityMexico City
Period8/20/228/24/22

Keywords

  • counterfactual explanations
  • machine learning
  • magnetic resonance imaging
  • radiomics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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