Literature DB >> 25093289

Development and internal validation of a prognostic model for survival after debulking surgery for epithelial ovarian cancer.

M J Rutten1, J H L Boldingh2, E Schuit3, H Trum4, W van Driel5, B W J Mol6, G G Kenter2, M R Buist2.   

Abstract

OBJECTIVE: Predicting survival of patients with epithelial ovarian cancer (EOC) is based on prognosis of the population. Combining prognostic factors could facilitate survival prediction on the level of the individual patient. The aim of this study was to develop a prognostic model to predict five-year disease specific survival in patients with EOC, and to evaluate whether this would add to prediction based on prognosis of the population. PATIENTS AND METHODS: A retrospective cohort study was performed of all EOC patients treated with primary debulking and adjuvant chemotherapy or neo-adjuvant chemotherapy and interval debulking surgery in three gynaecological-oncologic centres between 1998 and 2010. Primary outcome was 5-year disease-specific survival. We developed a Cox proportional hazard model using the LASSO-method to select the best combination of characteristics from 12 potential predictors and to correct for overfitting. Performance of the model was expressed as calibration and discrimination (c-statistic). A nomogram was developed to increase the clinical applicability of the model.
RESULTS: Of 840 patients with EOC 462 (55%) died within 5 years due to the disease. A combination of FIGO stage, residual tumour after surgery, primary or interval surgery, histology, performance status, age, amount of ascites and a family history suggestive of breast/ovarian cancer best predicted 5-year survival. The final model showed accurate calibration and the c-statistic was 0.71 (95% CI 0.69-0.74).
CONCLUSIONS: Five-year survival in all stage EOC patients can be predicted accurately using available characteristics. After external validation the model can be used for counselling of patients.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Debulking surgery; Nomogram; Ovarian cancer; Prediction; Survival

Mesh:

Year:  2014        PMID: 25093289     DOI: 10.1016/j.ygyno.2014.07.099

Source DB:  PubMed          Journal:  Gynecol Oncol        ISSN: 0090-8258            Impact factor:   5.482


  12 in total

1.  Chemotherapy for ovarian cancer in the Netherlands: a population-based study on treatment patterns and outcomes.

Authors:  E Houben; H G M van Haalen; W Sparreboom; J A Overbeek; N P M Ezendam; J M A Pijnenborg; J L Severens; M P P van Herk-Sukel
Journal:  Med Oncol       Date:  2017-02-21       Impact factor: 3.064

Review 2.  Impact of residual disease as a prognostic factor for survival in women with advanced epithelial ovarian cancer after primary surgery.

Authors:  Andrew Bryant; Shaun Hiu; Patience T Kunonga; Ketankumar Gajjar; Dawn Craig; Luke Vale; Brett A Winter-Roach; Ahmed Elattar; Raj Naik
Journal:  Cochrane Database Syst Rev       Date:  2022-09-26

3.  Assessment of published models and prognostic variables in epithelial ovarian cancer at Mayo Clinic.

Authors:  Andrea E Wahner Hendrickson; Kieran M Hawthorne; Ellen L Goode; Kimberly R Kalli; Krista M Goergen; Jamie N Bakkum-Gamez; William A Cliby; Gary L Keeney; Daniel W Visscher; Yaman Tarabishy; Ann L Oberg; Lynn C Hartmann; Matthew J Maurer
Journal:  Gynecol Oncol       Date:  2015-01-22       Impact factor: 5.482

4.  Reduced expression of SIRT2 in serous ovarian carcinoma promotes cell proliferation through disinhibition of CDK4 expression.

Authors:  Yanhua Du; Jun Wu; Haiyan Zhang; Shaobo Li; Hong Sun
Journal:  Mol Med Rep       Date:  2017-02-08       Impact factor: 2.952

5.  Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.

Authors:  E Sun Paik; Jeong Won Lee; Jeong Yeol Park; Ju Hyun Kim; Mijung Kim; Tae Joong Kim; Chel Hun Choi; Byoung Gie Kim; Duk Soo Bae; Sung Wook Seo
Journal:  J Gynecol Oncol       Date:  2019-03-11       Impact factor: 4.401

6.  Nomograms to Predict the Density of Tumor-Infiltrating Lymphocytes in Patients With High-Grade Serous Ovarian Cancer.

Authors:  Danian Dai; Lili Liu; He Huang; Shangqiu Chen; Bo Chen; Junya Cao; Xiaolin Luo; Feng Wang; Rongzhen Luo; Jihong Liu
Journal:  Front Oncol       Date:  2021-02-25       Impact factor: 6.244

7.  Association of Copy Number Variation Signature and Survival in Patients With Serous Ovarian Cancer.

Authors:  Ryon P Graf; Ramez Eskander; Leo Brueggeman; Dwayne G Stupack
Journal:  JAMA Netw Open       Date:  2021-06-01

8.  Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics.

Authors:  Meiying Zhang; Guanglei Zhuang; Xiangjun Sun; Yanying Shen; Aimin Zhao; Wen Di
Journal:  J Ovarian Res       Date:  2015-10-21       Impact factor: 4.234

9.  Nomograms Predicting Platinum Sensitivity, Progression-Free Survival, and Overall Survival Using Pretreatment Complete Blood Cell Counts in Epithelial Ovarian Cancer.

Authors:  E Sun Paik; Insuk Sohn; Sun-Young Baek; Minhee Shim; Hyun Jin Choi; Tae-Joong Kim; Chel Hun Choi; Jeong-Won Lee; Byoung-Gie Kim; Yoo-Young Lee; Duk-Soo Bae
Journal:  Cancer Res Treat       Date:  2016-09-27       Impact factor: 4.679

10.  Proteomics analysis to reveal biological pathways and predictive proteins in the survival of high-grade serous ovarian cancer.

Authors:  Hongyu Xie; Wenjie Wang; Fengyu Sun; Kui Deng; Xin Lu; Huijuan Liu; Weiwei Zhao; Yuanyuan Zhang; Xiaohua Zhou; Kang Li; Yan Hou
Journal:  Sci Rep       Date:  2017-08-29       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.