Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Predicting factors affecting the first recurrence of epithelial ovarian cancer using random survival forest

Wed Jul 24 10:24:05 2024

(2021) Predicting factors affecting the first recurrence of epithelial ovarian cancer using random survival forest. Acta Medica Iranica. pp. 504-509. ISSN 00446025 (ISSN)

Full text not available from this repository.

Official URL:


Predicting survival time has many Effective implications in life quality management for the remainder of the patient's life. Also, survival data are highly variable and make accurate predictions difficult or impossible. Random Survival Forest by repeated tree construction on Bootstrap samples and averaging on the results of these trees reduce the prediction error and cause further generalization of these results. In this retrospective study, the records of 141 patients with epithelial ovarian cancer who were referred to the oncology and radiotherapy ward of Imam Hossein Hospital in Tehran from 2007 to 2018 were used. Random Survival Forest was fitted to the data to investigate the key factors affecting the first recurrence of epithelial ovarian cancer. The mean age of the patients in our study was 52 (23-82) years and the median time to the first recurrence in these was 17 (0.5-127) months, respectively. According to RSF results, using variable importance criterion (VIMP) metastatic tumor with relative importance 2.665 and also using minimal (MD) by depth 2.349, tumor stage with relative importance 1.993 and depth 2.678, and maximum platelet count with relative importance 2.132 and depth 2.683 were the most important variables affecting in the first recurrence of Epithelial Ovarian Cancer. One of the disadvantages of classical methods is the inappropriate fitting of many variables and the need for specific assumptions. More advanced methods such as RSF without the need for any specific assumptions with less prediction error can well explain event variations when exposed to high-dimensional data. © 2021 Tehran University of Medical Sciences. All rights reserved.

Item Type: Article
Keywords: Epithelial ovarian cancer First recurrent Random survival forest hemoglobin accuracy adult advanced cancer aged Article bootstrapping cancer prognosis cancer radiotherapy cancer recurrence cancer staging cancer survival chemotherapy controlled study decision tree diagnostic test accuracy study error female follow up histology human human tissue incidence leukocyte count machine learning major clinical study metastasis middle aged ovary cancer ovary carcinoma overall survival platelet count prediction predictive value progression free survival quality of life random forest retrospective study support vector machine survival time total quality management variable importance criterion
Page Range: pp. 504-509
Journal or Publication Title: Acta Medica Iranica
Journal Index: Scopus
Volume: 59
Number: 8
ISSN: 00446025 (ISSN)
Depositing User: مهندس مهدی شریفی

Actions (login required)

View Item View Item