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

Simple Summary In the era of personalized medicine, Artificial Intelligence (AI) has emerged as a powerful tool with growing applications in the field of gynaecologic oncology. However, AI applications are encountered with several challenges derived from their "black-box" nature, which limits their adoption by clinicians. Surgical decision-making at cytoreductive surgery for epithelial ovarian cancer (EOC) is a complex matter, and an accurate prediction of surgical effort is required to ensure the good health and care of patients. We combined high-performance AI modeling with an eXplainable Artificial Intelligence (XAI) framework to explain feature effects and interactions associated with specific threshold surgical effort using data from a single public institution. We revealed features not routinely measured in the clinical practice, including human factors that could be responsible for the variation in the surgical effort. Selective decreased surgical effort may be associated with the surgeon's age. The use of XAI frameworks can provide actionable information for surgeons to improve patient outcomes in gynaecologic oncology. (1) Background: Surgical cytoreduction for epithelial ovarian cancer (EOC) is a complex procedure. Encompassed within the performance skills to achieve surgical precision, intra-operative surgical decision-making remains a core feature. The use of eXplainable Artificial Intelligence (XAI) could potentially interpret the influence of human factors on the surgical effort for the cytoreductive outcome in question; (2) Methods: The retrospective cohort study evaluated 560 consecutive EOC patients who underwent cytoreductive surgery between January 2014 and December 2019 in a single public institution. The eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) algorithms were employed to develop the predictive model, including patient- and operation-specific features, and novel features reflecting human factors in surgical heuristics. The precision, recall, F1 score, and area under curve (AUC) were compared between both training algorithms. The SHapley Additive exPlanations (SHAP) framework was used to provide global and local explainability for the predictive model; (3) Results: A surgical complexity score (SCS) cut-off value of five was calculated using a Receiver Operator Characteristic (ROC) curve, above which the probability of incomplete cytoreduction was more likely (area under the curve [AUC] = 0.644; 95% confidence interval [CI] = 0.598-0.69; sensitivity and specificity 34.1%, 86.5%, respectively; p = 0.000). The XGBoost outperformed the DNN assessment for the prediction of the above threshold surgical effort outcome (AUC = 0.77; 95% [CI] 0.69-0.85; p < 0.05 vs. AUC 0.739; 95% [CI] 0.655-0.823; p < 0.95). We identified "turning points" that demonstrated a clear preference towards above the given cut-off level of surgical effort; in consultant surgeons with <12 years of experience, age <53 years old, who, when attempting primary cytoreductive surgery, recorded the presence of ascites, an Intraoperative Mapping of Ovarian Cancer score >4, and a Peritoneal Carcinomatosis Index >7, in a surgical environment with the optimization of infrastructural support. (4) Conclusions: Using XAI, we explain how intra-operative decisions may consider human factors during EOC cytoreduction alongside factual knowledge, to maximize the magnitude of the selected trade-off in effort. XAI techniques are critical for a better understanding of Artificial Intelligence frameworks, and to enhance their incorporation in medical applications.