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Title: Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer
Authors: Laios, Alexandros
Kalampokis, Evangelos
Johnson, Racheal
Munot, Sarika
Thangavelu, Amudha
Hutson, Richard
Broadhead, Tim
Theophilou, Georgios
Leach, Chris
Nugent, David
De Jong, Diederick
Type: Article
Subjects: FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
FRASCATI::Medical and Health sciences::Clinical medicine
Keywords: Explainable Artificial Intelligence
complete cytoreduction
epithelial ovarian cancer
human factors
surgical complexity score
Issue Date: 15-Jul-2022
Source: Cancers
Volume: 14
Issue: 14
First Page: 3447
Abstract: (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.
ISSN: 2072-6694
Other Identifiers: 10.3390/cancers14143447
Appears in Collections:Department of Business Administration

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