{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:36:23Z","timestamp":1787027783035,"version":"build-2736575974"},"reference-count":67,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T00:00:00Z","timestamp":1657843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>(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\u20130.69; 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\u20130.85; p &lt; 0.05 vs. AUC 0.739; 95% [CI] 0.655\u20130.823; p &lt; 0.95). We identified \u201cturning points\u201d that demonstrated a clear preference towards above the given cut-off level of surgical effort; in consultant surgeons with &lt;12 years of experience, age &lt;53 years old, who, when attempting primary cytoreductive surgery, recorded the presence of ascites, an Intraoperative Mapping of Ovarian Cancer score &gt;4, and a Peritoneal Carcinomatosis Index &gt;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.<\/jats:p>","DOI":"10.3390\/cancers14143447","type":"journal-article","created":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T21:00:28Z","timestamp":1658091628000},"page":"3447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4870-7393","authenticated-orcid":false,"given":"Alexandros","family":"Laios","sequence":"first","affiliation":[{"name":"Department of Gynaecologic Oncology, St James\u2019s University Hospital, Leeds LS9 7TF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4416-8764","authenticated-orcid":false,"given":"Evangelos","family":"Kalampokis","sequence":"additional","affiliation":[{"name":"Information Systems Lab, University of Macedonia, 54636 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2677-2709","authenticated-orcid":false,"given":"Racheal","family":"Johnson","sequence":"additional","affiliation":[{"name":"Department of Gynaecologic Oncology, St James\u2019s University Hospital, Leeds LS9 7TF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarika","family":"Munot","sequence":"additional","affiliation":[{"name":"Department of Gynaecologic Oncology, St James\u2019s University Hospital, Leeds LS9 7TF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amudha","family":"Thangavelu","sequence":"additional","affiliation":[{"name":"Department of Gynaecologic Oncology, St James\u2019s University Hospital, Leeds LS9 7TF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Hutson","sequence":"additional","affiliation":[{"name":"Department of Gynaecologic Oncology, St James\u2019s University Hospital, Leeds LS9 7TF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim","family":"Broadhead","sequence":"additional","affiliation":[{"name":"Department of Gynaecologic Oncology, St James\u2019s University Hospital, Leeds LS9 7TF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Theophilou","sequence":"additional","affiliation":[{"name":"Department of Gynaecologic Oncology, St James\u2019s University Hospital, Leeds LS9 7TF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris","family":"Leach","sequence":"additional","affiliation":[{"name":"School of Human & Health Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK"},{"name":"Department of Psychology Services, South West Yorkshire Mental Health NHS Foundation Trust, The Laura Mitchell Health & Wellbeing Centre, Halifax HX1 1YR, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Nugent","sequence":"additional","affiliation":[{"name":"Department of Gynaecologic Oncology, St James\u2019s University Hospital, Leeds LS9 7TF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0081-674X","authenticated-orcid":false,"given":"Diederick","family":"De Jong","sequence":"additional","affiliation":[{"name":"Department of Gynaecologic Oncology, St James\u2019s University Hospital, Leeds LS9 7TF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21551","article-title":"Cancer statistics, 2019","volume":"69","author":"Siegel","year":"2019","journal-title":"CA Cancer J. 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