Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer
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 predic...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/12/4/607 |
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| author | Alexandros Laios Evangelos Kalampokis Racheal Johnson Amudha Thangavelu Constantine Tarabanis David Nugent Diederick De Jong |
| author_facet | Alexandros Laios Evangelos Kalampokis Racheal Johnson Amudha Thangavelu Constantine Tarabanis David Nugent Diederick De Jong |
| author_sort | Alexandros Laios |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:30:48Z |
| format | Article |
| id | doaj.art-5e335b7457c84b8792820b7c726989e0 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T04:30:48Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-5e335b7457c84b8792820b7c726989e02023-12-03T13:35:28ZengMDPI AGJournal of Personalized Medicine2075-44262022-04-0112460710.3390/jpm12040607Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian CancerAlexandros Laios0Evangelos Kalampokis1Racheal Johnson2Amudha Thangavelu3Constantine Tarabanis4David Nugent5Diederick De Jong6Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UKDepartment of Business Administration, University of Macedonia, 54636 Thessaloniki, GreeceDepartment of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UKDepartment of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UKDepartment of Internal Medicine, School of Medicine, New York University, NYU, Langone Health, New York, NY 10016, USADepartment of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UKDepartment of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UKComplete 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.https://www.mdpi.com/2075-4426/12/4/607Explainable Artificial Intelligencecomplete cytoreductionepithelial ovarian cancer |
| spellingShingle | Alexandros Laios Evangelos Kalampokis Racheal Johnson Amudha Thangavelu Constantine Tarabanis David Nugent Diederick De Jong Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer Journal of Personalized Medicine Explainable Artificial Intelligence complete cytoreduction epithelial ovarian cancer |
| title | Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer |
| title_full | Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer |
| title_fullStr | Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer |
| title_full_unstemmed | Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer |
| title_short | Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer |
| title_sort | explainable artificial intelligence for prediction of complete surgical cytoreduction in advanced stage epithelial ovarian cancer |
| topic | Explainable Artificial Intelligence complete cytoreduction epithelial ovarian cancer |
| url | https://www.mdpi.com/2075-4426/12/4/607 |
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