{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T22:10:26Z","timestamp":1790892626192,"version":"4.1.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"vor","delay-in-days":36,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) is associated with improved survival in recurrent ovarian cancer (ROC) but carries a high risk of postoperative complications. Accurate perioperative risk stratification remains an unmet need.  To develop and internally validate a perioperative risk model for postoperative complications in ROC patients using information available by the end of surgery (pre- and intra-operative data), and to compare logistic regression (LR) and artificial neural networks (ANN) as possible predictive models.  A retrospective analysis of 71 patients treated with CRS and HIPEC between 2011 and 2022 was performed. Clinical, surgical, and perioperative variables were analysed. Predictors were restricted to variables available at or before the end of the index operation, which prevents information leakage by using only data available at prediction time. LR and ANN models were developed and assessed with cross-validation. Performance reporting followed TRIPOD (Type b) and TRIPOD\u2009+\u2009AI, with Brier score, and calibration slope\/intercept from out\u2011of\u2011fold (OOF) predictions. Thresholded metrics (accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve, AUC) were summarised at a prespecified probability cut-off. Exploratory univariate odds ratio analyses with Holm-adjusted p-values were used to explore procedure\u2013complication associations.  Postoperative complications occurred in 45% of patients. LR identified blood loss (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.005) and number of procedures (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.042) as significant predictors of complications. The LR model achieved an accuracy of 66.2%, precision of 64.3%, recall of 56.2%, F1 score of 60.0%, and AUC of 0.700. The ANN model achieved an accuracy of 97.2%, precision of 94.3%, recall of 100%, F1 score of 97.1%, and AUC of 0.967. Hysterectomy with adnexa (OR\u2009=\u200911.67,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.035) and metastasectomy (OR\u2009=\u20097.42,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.042) were significantly associated with higher postoperative complication rates.  ANN demonstrated superior predictive performance compared to LR in identifying postoperative complications after CRS and HIPEC, as indicated by ROC analysis. Combining traditional statistical modelling with modern machine learning may enhance ROC for perioperative risk stratification after CRS with HIPEC. A well-calibrated, interpretable LR model together with a highly discriminative ANN could enable more tailored allocation of intensive care resources and earlier identification of high-risk patients, potentially improving the safety of this demanding but beneficial treatment. However, external multicentre validation is required before clinical implementation.\n                  <\/jats:p>","DOI":"10.1038\/s41598-025-33049-9","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T18:52:59Z","timestamp":1766083979000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Risk stratification for postoperative complications after CRS and HIPEC in recurrent ovarian cancer patients: a comparative analysis of logistic regression and machine learning models"],"prefix":"10.1038","volume":"16","author":[{"given":"Jakub","family":"Litwi\u0144ski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katarzyna","family":"G\u0119ca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Izabela","family":"\u015awietlicka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u0141ukasz","family":"Wi\u015bniowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wojciech","family":"Polkowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magdalena","family":"Sk\u00f3rzewska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"issue":"3","key":"33049_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H. et al. 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