{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T11:51:56Z","timestamp":1790941916278,"version":"4.1.0"},"reference-count":40,"publisher":"Baishideng Publishing Group Inc.","issue":"23","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["World J Gastroenterol"],"abstract":"BACKGROUND<\/jats:p>\n Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.<\/jats:p>\n AIM<\/jats:p>\n To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.<\/jats:p>\n METHODS<\/jats:p>\n Data of patients treated for colorectal cancer (n = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group (n = 60) and a control group (n = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model.<\/jats:p>\n RESULTS<\/jats:p>\n More patients in the experimental group were \u2265 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (P < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.<\/jats:p>\n CONCLUSION<\/jats:p>\n This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.<\/jats:p>","DOI":"10.3748\/wjg.v30.i23.2991","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T02:00:40Z","timestamp":1718848840000},"page":"2991-3004","source":"Crossref","is-referenced-by-count":11,"title":["Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer"],"prefix":"10.3748","volume":"30","author":[{"given":"Li-Qun","family":"Cai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Da-Qing","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong-Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Xiong","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2060","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21601"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21338"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1080\/19490976.2023.2236364"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.12998\/wjcc.v9.i24.6964"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.22.01690"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.29271\/jcpsp.2022.09.1170"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1177\/1553350621997761"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-015-4271-1"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1111\/ases.12452"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-023-10227-2"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.5604\/01.3001.0013.1922"},{"key":"B13","unstructured":"Yin SQ, Liang X, Wu XH, Yang J, Wang A. 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