scholarly journals Predictive significance of preoperative CT findings for suboptimal cytoreduction in advanced ovarian cancer: a meta-analysis

2018 ◽  
Vol Volume 10 ◽  
pp. 2019-2030 ◽  
Author(s):  
Ting Wen Yi Hu ◽  
Dan Nie ◽  
Jin Hai Gou ◽  
Zheng Yu Li
Keyword(s):  
Ovarian Cancer ◽  
Meta Analysis ◽  
Advanced Ovarian Cancer ◽  
Ct Findings ◽  
Preoperative Ct

Poly(ADP-ribose) polymerase inhibitors as maintenance treatment in patients with newly diagnosed advanced ovarian cancer: a meta-analysis

2020 ◽  
Vol 16 (10) ◽  
pp. 585-596 ◽  
Author(s):  
Ezzeldin M Ibrahim ◽  
Ahmed A Refae ◽  
Ali M Bayer ◽  
Emad R Sagr
Keyword(s):  
Ovarian Cancer ◽  
Randomized Clinical Trials ◽  
Brca Mutation ◽  
Meta Analysis ◽  
Advanced Ovarian Cancer ◽  
Progression Free Survival ◽  
Recurrent Ovarian Cancer ◽  
Newly Diagnosed ◽  
Free Survival ◽  
Polymerase Inhibitors

Aim: Poly(ADP-ribose) polymerase inhibitors (PARPIs) improved progression-free survival among patients with recurrent ovarian cancer. This meta-analysis examined the effectiveness of PARPIs as maintenance strategy for newly diagnosed patients with advanced high-grade ovarian cancer with or without mutations. Materials & methods: Using defined selection criteria, a literature search identified four eligible randomized clinical trials involving 2386 patients. Results: Compared with placebo maintenance, PARPIs achieved a 46% reduction in the risk of progression or death as compared with placebo (hazard ratio: 0.54; 95% CI: 0.39–0.73; p < 0.0001). That benefit was shown in all clinical subgroups: among those with BRCA mutation, with negative/unknown BRCA mutation, and in those with homologous recombination deficient tumors. Data about the effect on overall survival are still premature. Conclusion: In patients with newly diagnosed advanced ovarian cancer, PARPIs maintenance after standard therapy achieved a significant improvement in progression-free survival as compared with placebo, overall and in all subgroups.

Multi-Institutional Reciprocal Validation Study of Computed Tomography Predictors of Suboptimal Primary Cytoreduction in Patients With Advanced Ovarian Cancer

2007 ◽  
Vol 25 (4) ◽  
pp. 384-389 ◽  
Author(s):  
Allison E. Axtell ◽  
Margaret H. Lee ◽  
Robert E. Bristow ◽  
Sean C. Dowdy ◽  
William A. Cliby ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ovarian Cancer ◽  
Predictive Models ◽  
Cross Validation ◽  
Residual Disease ◽  
Advanced Ovarian Cancer ◽  
High Accuracy ◽  
Ct Scans ◽  
Preoperative Ct ◽  
Accuracy Rates

Purpose Identify features on preoperative computed tomography (CT) scans to predict suboptimal primary cytoreduction in patients treated for advanced ovarian cancer in institution A. Reciprocally cross validate the predictors identified with those from two previously published cohorts from institutions B and C. Patients and Methods Preoperative CT scans from patients with stage III/IV epithelial ovarian cancer who underwent primary cytoreduction in institution A between 1999 and 2005 were retrospectively reviewed by radiologists blinded to surgical outcome. Fourteen criteria were assessed. Crossvalidation was performed by applying predictive model A to the patients from cohorts B and C, and reciprocally applying predictive models B and C to cohort A. Results Sixty-five patients from institution A were included. The rate of optimal cytoreduction (≤ 1 cm residual disease) was 78%. Diaphragm disease and large bowel mesentery implants were the only CT predictors of suboptimal cytoreduction on univariate (P < .02) and multivariate analysis (P < .02). In combination (model A), these predictors had a sensitivity of 79%, a specificity of 75%, and an accuracy of 77% for suboptimal cytoreduction. When model A was applied to cohorts B and C, accuracy rates dropped to 34% and 64%, respectively. Reciprocally, models B and C had accuracy rates of 93% and 79% in their original cohorts, which fell to 74% and 48% in cohort A. Conclusion The high accuracy rates of CT predictors of suboptimal cytoreduction in the original cohorts could not be confirmed in the cross validation. Preoperative CT predictors should be used with caution when deciding between surgical cytoreduction and neoadjuvant chemotherapy.

Effect of Obesity on Survival of Women With Epithelial Ovarian Cancer: A Systematic Review and Meta-Analysis of Observational Studies

2011 ◽  
Vol 21 (9) ◽  
pp. 1525-1532 ◽  
Author(s):  
Hyun-Sik Yang ◽  
Chan Yoon ◽  
Seung-Kwon Myung ◽  
Sang Min Park
Keyword(s):  
Ovarian Cancer ◽  
Confidence Interval ◽  
Epithelial Ovarian Cancer ◽  
Meta Analysis ◽  
Early Adulthood ◽  
Harmful Effect ◽  
Advanced Ovarian Cancer ◽  
Final Analysis ◽  
Cochrane Central Register ◽  
Central Register

ObjectiveAlthough obesity is shown to be a risk factor for epithelial ovarian cancer, its role as a prognostic factor has been remained inconclusive. In this study, available evidences on this matter to date have been assembled for a meta-analysis to determine the effect of obesity on the survival of patients with epithelial ovarian cancer.Materials and MethodsEligible studies published up to December 2010 were searched using MEDLINE (PubMed), EMBASE, and Cochrane Central Register of Controlled Trials, and manual review of relevant bibliography to look for additional studies was done. Adjusted hazard ratios (HRs) from individual studies were pooled using a random-effects model.ResultsTen cohort studies of 331 screened articles were included in the final analysis. The meta-analysis showed overweight or obesity at early adulthood to be associated with higher mortality among patients with ovarian cancer (HR, 1.60; 95% confidence interval, 1.10–2.34). Among patients with advanced ovarian cancer, premorbid obesity was associated with worse prognosis (HR, 1.45; 95% confidence interval, 1.09–1.93). However, there was no significant relationship between prognosis and obesity around the time of diagnosis.ConclusionsThis study suggests a possible relationship between obesity at early adulthood and higher mortality among patients with ovarian cancer. Further studies are needed to elucidate the harmful effect of obesity on the survival of patients with ovarian cancer.

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