{"title":"Random survival forest model in patients with epithelial ovarian cancer: a study based on SEER database and single center data.","authors":"Luwei Wei, Guowei Chen, Huiying Liang, Li Li","doi":"10.62347/PLDH8547","DOIUrl":null,"url":null,"abstract":"<p><p>Clinical data of 1,780 patients with epithelial ovarian carcinoma (EOC) in the Surveillance, Epidemiology and End Results (SEER) database were retrospectively analyzed. A random survival forest model and a nomogram model were built based on the prognostic factors. The clinical data of 140 patients with EOC treated in Liuzhou Worker's Hospital were collected for the validation of the prognostic model. Age (≥75 years), histology grade (poor differentiation or undifferentiation), histologic types (clear cell carcinoma or carcinosarcoma), T stage (T2 or T3), M stage (M1), surgical conditions, and chemotherapy situation (without chemotherapy) were identified as independent risk factors. Based on these factors, a random forest survival prediction model was established. In the training set, the area under the curve (AUC) for the random forest survival prediction model in predicting 1-, 3- and 5-year survival were 0.848, 0.859 and 0.890, respectively. In the test set, the AUCs for 1-, 3- and 5-year survival were 0.992, 0.795 and 0.883, respectively. A nomogram prediction model was also established. In the training set, the AUCs for the nomogram prediction model for 1-, 3- and 5-year survival were 0.789, 0.803 and 0.838, respectively. In the test set, the AUCs for 1-, 3- and 5-year survival were 0.926, 0.748 and 0.836, respectively. The results indicated that the random forest survival model established in this study holds significant clinical value. Physicians can develop personalized follow-up strategies or treatment regimens for patients based on the predicted survival risk, potentially improving long-term outcomes.</p>","PeriodicalId":7437,"journal":{"name":"American journal of cancer research","volume":"15 2","pages":"769-780"},"PeriodicalIF":3.6000,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11897640/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.62347/PLDH8547","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Clinical data of 1,780 patients with epithelial ovarian carcinoma (EOC) in the Surveillance, Epidemiology and End Results (SEER) database were retrospectively analyzed. A random survival forest model and a nomogram model were built based on the prognostic factors. The clinical data of 140 patients with EOC treated in Liuzhou Worker's Hospital were collected for the validation of the prognostic model. Age (≥75 years), histology grade (poor differentiation or undifferentiation), histologic types (clear cell carcinoma or carcinosarcoma), T stage (T2 or T3), M stage (M1), surgical conditions, and chemotherapy situation (without chemotherapy) were identified as independent risk factors. Based on these factors, a random forest survival prediction model was established. In the training set, the area under the curve (AUC) for the random forest survival prediction model in predicting 1-, 3- and 5-year survival were 0.848, 0.859 and 0.890, respectively. In the test set, the AUCs for 1-, 3- and 5-year survival were 0.992, 0.795 and 0.883, respectively. A nomogram prediction model was also established. In the training set, the AUCs for the nomogram prediction model for 1-, 3- and 5-year survival were 0.789, 0.803 and 0.838, respectively. In the test set, the AUCs for 1-, 3- and 5-year survival were 0.926, 0.748 and 0.836, respectively. The results indicated that the random forest survival model established in this study holds significant clinical value. Physicians can develop personalized follow-up strategies or treatment regimens for patients based on the predicted survival risk, potentially improving long-term outcomes.
期刊介绍:
The American Journal of Cancer Research (AJCR) (ISSN 2156-6976), is an independent open access, online only journal to facilitate rapid dissemination of novel discoveries in basic science and treatment of cancer. It was founded by a group of scientists for cancer research and clinical academic oncologists from around the world, who are devoted to the promotion and advancement of our understanding of the cancer and its treatment. The scope of AJCR is intended to encompass that of multi-disciplinary researchers from any scientific discipline where the primary focus of the research is to increase and integrate knowledge about etiology and molecular mechanisms of carcinogenesis with the ultimate aim of advancing the cure and prevention of this increasingly devastating disease. To achieve these aims AJCR will publish review articles, original articles and new techniques in cancer research and therapy. It will also publish hypothesis, case reports and letter to the editor. Unlike most other open access online journals, AJCR will keep most of the traditional features of paper print that we are all familiar with, such as continuous volume, issue numbers, as well as continuous page numbers to retain our comfortable familiarity towards an academic journal.