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Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian

Alexandros Laios1, Evangelos Kalampokis2, Racheal Johnson1

  • 1Department of Gynaecologic Oncology, St James's University Hospital, Leeds LS9 7TF, UK.

Cancers
|July 27, 2022
PubMed
Summary

eXplainable Artificial Intelligence (XAI) can predict surgical effort in epithelial ovarian cancer (EOC) cytoreduction. Human factors and surgical heuristics significantly influence outcomes, guiding better decision-making for improved patient results.

Keywords:
Explainable Artificial Intelligencecomplete cytoreductionepithelial ovarian cancerhuman factorssurgical complexity score

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Area of Science:

  • Oncology
  • Surgical Oncology
  • Artificial Intelligence in Medicine

Background:

  • Epithelial ovarian cancer (EOC) cytoreductive surgery demands high precision and complex intra-operative decision-making.
  • Human factors significantly influence surgical effort and outcomes in cytoreductive procedures.
  • eXplainable Artificial Intelligence (XAI) offers a potential method to interpret these human factors.

Purpose of the Study:

  • To develop and evaluate predictive models for surgical effort in EOC cytoreduction using machine learning.
  • To identify key patient- and operation-specific features, including human factors, influencing surgical outcomes.
  • To leverage XAI for understanding the impact of intra-operative decision-making on cytoreductive success.

Main Methods:

  • A retrospective cohort study of 560 EOC patients undergoing cytoreductive surgery.
  • Development of predictive models using eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) algorithms.
  • Application of the SHapley Additive exPlanations (SHAP) framework for model interpretability.

Main Results:

  • A surgical complexity score (SCS) cut-off of five predicted a higher likelihood of incomplete cytoreduction (AUC = 0.644).
  • XGBoost demonstrated superior predictive performance compared to DNN (AUC = 0.77 vs. 0.739).
  • Key factors influencing surgical effort included surgeon experience, patient age, ascites, and intraoperative scores (IMOC, PCI).

Conclusions:

  • XAI can elucidate how human factors inform intra-operative decisions in EOC cytoreduction.
  • Integrating XAI enhances the understanding and application of AI in surgical contexts.
  • XAI facilitates maximizing desired trade-offs in surgical effort for improved outcomes.