{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:33:57Z","timestamp":1771486437715,"version":"3.50.1"},"reference-count":20,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Surgical Oncology"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To develop a machine learning (ML) algorithm to predict outcome of primary cytoreductive surgery (PCS) in patients with advanced ovarian cancer (AOC)<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective cohort study included patients with AOC undergoing PCS between January 2017 and February 2021. Using radiologic criteria, patient factors (age, CA\u2010125, performance status, BRCA) and surgical complexity scores, we trained a random forest model to predict the dichotomous outcome of optimal cytoreduction (&lt;1\u2009cm) and no gross residual (RD\u2009=\u20090\u2009mm) using JMP\u2010Pro 15 (SAS). This model is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/ipm-ml.ccm.sickkids.ca\">https:\/\/ipm-ml.ccm.sickkids.ca<\/jats:ext-link>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>One hundred and fifty\u2010one patients underwent PCS and randomly assigned to train (<jats:italic>n<\/jats:italic>\u2009=\u200992), validate (<jats:italic>n<\/jats:italic>\u2009=\u200930), or test (<jats:italic>n<\/jats:italic>\u2009=\u200929) the model. The median age was 58 (27\u201383). Patients with suboptimal cytoreduction were more likely to have an Eastern Cooperative Oncology Group 3\u20134 (11% vs. 0.75%, <jats:italic>p<\/jats:italic>\u2009=\u20090.004), lower albumin (38 vs. 41,\u00a0<jats:italic>p<\/jats:italic>\u2009=\u20090.02), and higher CA125 (1126 vs. 388,\u00a0<jats:italic>p<\/jats:italic>\u2009=\u20090.012) than patients with optimal cytoreduction (<jats:italic>n<\/jats:italic>\u2009=\u2009133). There were no significant differences in age, histology, stage, or BRCA status between groups. The bootstrap random forest model had AUCs of 99.8% (training), 89.6%(validation), and 89.0% (test). The top five\u00a0contributors were CA125, albumin, diaphragmatic disease, age, and ascites. For RD\u2009=\u20090\u2009mm, the AUCs were 94.4%, 52%, and 84%, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our ML algorithm demonstrated high accuracy in predicting optimal cytoreduction in patients with AOC selected for PCS and may assist decision\u2010making.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jso.27137","type":"journal-article","created":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T13:55:55Z","timestamp":1668002155000},"page":"465-472","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Using a machine learning algorithm to predict outcome of primary cytoreductive surgery in advanced ovarian cancer"],"prefix":"10.1002","volume":"127","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0651-4283","authenticated-orcid":false,"given":"Sabrina","family":"Piedimonte","sequence":"first","affiliation":[{"name":"Gynecologic Oncology University of Toronto  Toronto Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lauren","family":"Erdman","sequence":"additional","affiliation":[{"name":"Computer Science The Hospital for Sick Children  Toronto Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Delvin","family":"So","sequence":"additional","affiliation":[{"name":"Computer Science The Hospital for Sick Children  Toronto Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7022-8308","authenticated-orcid":false,"given":"Marcus Q.","family":"Bernardini","sequence":"additional","affiliation":[{"name":"Gynecologic Oncology University Health Network  Toronto Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah E.","family":"Ferguson","sequence":"additional","affiliation":[{"name":"Division of Gynecologic Oncology Princess Margaret Hospital Cancer Centre  Toronto Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephane","family":"Laframboise","sequence":"additional","affiliation":[{"name":"Obsterics and Gynecology University of Toronto  Toronto Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Genevieve","family":"Bouchard Fortier","sequence":"additional","affiliation":[{"name":"Obsterics and Gynecology University of Toronto  Toronto Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6038-9906","authenticated-orcid":false,"given":"Paulina","family":"Cybulska","sequence":"additional","affiliation":[{"name":"Obsterics and Gynecology University of Toronto  Toronto Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taymaa","family":"May","sequence":"additional","affiliation":[{"name":"Obstetrics and Gynecology University of Toronto Faculty of Medicine  Toronto Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liat","family":"Hogen","sequence":"additional","affiliation":[{"name":"Obsterics and Gynecology University of Toronto  Toronto Ontario Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2007.13.1953"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/IGC.0000000000000946"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2014.56.3106"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2020.06.037"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2019.04.011"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2020.05.041"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2019.08.004"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-77220-w"},{"key":"e_1_2_10_10_1","unstructured":"The difference between artificial intelligence machine learning and deep learning. 2019. Accessed May 28 2021.https:\/\/medium.com\/iotforall\/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991"},{"key":"e_1_2_10_11_1","unstructured":"HastingT TibshiraniR FriedmanJ.The elements for statistical learning: data mining inference and prediction.2nded.Springer.2009."},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2022.06.026"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2007.05.032"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2017.02.020"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2020.12.026"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2007.10.495"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1245\/ASO.2006.08.021"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2020.10.004"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.suronc.2019.04.008"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1097\/CEJ.0000000000000432"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2021.39.15_suppl.5543"}],"container-title":["Journal of Surgical Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jso.27137","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/jso.27137","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jso.27137","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,20]],"date-time":"2023-08-20T06:02:08Z","timestamp":1692511328000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jso.27137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,9]]},"references-count":20,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["10.1002\/jso.27137"],"URL":"https:\/\/doi.org\/10.1002\/jso.27137","archive":["Portico"],"relation":{},"ISSN":["0022-4790","1096-9098"],"issn-type":[{"value":"0022-4790","type":"print"},{"value":"1096-9098","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,9]]},"assertion":[{"value":"2022-06-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-10-21","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-11-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}