Preliminary outcomes of five-year survival for ovarian malignancies in profiled Serbian Oncology Centre

塞尔维亚重点肿瘤中心卵巢恶性肿瘤五年生存率的初步结果

阅读:1

Abstract

OBJECTIVE: The present study purposed to determine characteristics of ovarian carcinoma and to analyze predictors of survival in patients with ovarian carcinoma. METHOD: A retrospective cohort study was conducted including the patients with diagnosed ovarian carcinoma treated at the Clinic for Operative Oncology, Oncology Institute of Vojvodina in the period from January 2012 to December 2016. Seventy-two women with ovarian carcinoma were included in the analysis. The data about the histological type of tumor, disease stage, treatment, lymphatic infiltration, and surgical procedure were collected retrospectively, using the database of the institution where the research was conducted (BirPis 21 SRC Infonet DOO ‒ Information System Oncology Institute of Vojvodina). Descriptive statistics and multivariate analysis using Cox proportional hazards model were performed. RESULTS: The univariate Cox regression analysis identified histology, tumor grade, FIGO (International Federation of Gynecology and Obstetrics) stage, NACT (Neoadjuvant Chemotherapy), number of therapy cycles, type of surgery, and chemotherapy response as independent predictors of mortality. Finally, the type of tumor and chemotherapy response had an increased hazard ratio for mortality in the multivariate Cox regression model. Herewith, the percentage of high-grade, advanced-stage ovarian cancer patients with complete response to chemotherapy, absence of recurrent disease, and lymphovascular space invasion were significant predictors of survival in patients with ovarian carcinoma. CONCLUSIONS: Herein, emerging data regarding precision medicine and molecular-based personalized treatments are promising and will likely modify the way the authors provide multiple lines of treatments in the near future.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。