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Title: Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer
Authors: Laios, Alexandros
Kalampokis, Evangelos
Johnson, Racheal
Thangavelu, Amudha
Tarabanis, Constantine
Nugent, David
De Jong, Diederick
Type: Article
Subjects: FRASCATI::Medical and Health sciences::Clinical medicine
FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
Keywords: Explainable Artificial Intelligence
complete cytoreduction
epithelial ovarian cancer
Issue Date: 10-Apr-2022
Source: Journal of personalized medicine
Volume: 12
Issue: 4
First Page: 607
Abstract: Complete surgical cytoreduction (R0 resection) is the single most important prognosticator in epithelial ovarian cancer (EOC). Explainable Artificial Intelligence (XAI) could clarify the influence of static and real-time features in the R0 resection prediction. We aimed to develop an AI-based predictive model for the R0 resection outcome, apply a methodology to explain the prediction, and evaluate the interpretability by analysing feature interactions. The retrospective cohort finally assessed 571 consecutive advanced-stage EOC patients who underwent cytoreductive surgery. An eXtreme Gradient Boosting (XGBoost) algorithm was employed to develop the predictive model including mostly patient- and surgery-specific variables. The Shapley Additive explanations (SHAP) framework was used to provide global and local explainability for the predictive model. The XGBoost accurately predicted R0 resection (area under curve [AUC] = 0.866; 95% confidence interval [CI] = 0.8-0.93). We identified "turning points" that increased the probability of complete cytoreduction including Intraoperative Mapping of Ovarian Cancer Score and Peritoneal Carcinomatosis Index < 4 and <5, respectively, followed by Surgical Complexity Score > 4, patient's age < 60 years, and largest tumour bulk < 5 cm in a surgical environment of optimized infrastructural support. We demonstrated high model accuracy for the R0 resection prediction in EOC patients and provided novel global and local feature explainability that can be used for quality control and internal audit.
ISSN: 2075-4426
Other Identifiers: 10.3390/jpm12040607
Appears in Collections:Department of Business Administration

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