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Consolidation strategies in ovarian cancer: observations for future clinical trials.

机译:卵巢癌的巩固策略:对未来临床试验的观察。

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PURPOSE.: To describe the characteristics of a series of study populations of ovarian cancer patients with identical eligibility criteria in second or subsequent clinical remission (cCR) and to propose endpoint benchmarks for future consolidation studies. PATIENTS AND METHODS.: The patient populations consisted of those (1) untreated (U; observed until progression; n=35, (2) receiving imatinib (G; n=32), (3) receiving goserelin and bicalutamide (A; n=32), and (4) receiving vaccine (V; n=68; total=167). The endpoint of the combined analysis was progression-free survival in second remission (PFS 2). Patient characteristics were compared by chi-square test, and factors predicting PFS 2 evaluated in multivariate Cox model. RESULTS.: Groups were comparable for age, stage, grade, and debulking. Multivariate model to predict PFS 2 duration included histology, stage, optimal debulking, PFS 1 duration, and the type of intervention. As a benchmark for future studies, the median PFS 2 of the combined population of G, A, and U (removing V which had the most impact in prolonging PFS 2, n=68) was 11.3 months (95% CI: 10.4-12.5 months). The percent of patients with PFS 2>PFS 1 was 14/90 (16%). At 12 months, 43% remain progression-free. CONCLUSION.: Preliminary benchmarks for efficacy endpoints are suggested for future consolidation trials of patients in cCR. However, the suggested strategies will require validation in randomized trials and larger data sets.
机译:目的:描述具有相同入组标准的卵巢癌患者的一系列研究人群在第二次或以后的临床缓解(cCR)中的特征,并为将来的合并研究提出终点基准。患者和方法:患者人群包括(1)未经治疗(U;观察到进展; n = 35,(2)接受伊马替尼(G; n = 32),(3)接受戈舍瑞林和比卡鲁胺(A; n)。 = 32)和(4)接受疫苗(V; n = 68;总数= 167)。联合分析的终点是第二次缓解期的无进展生存期(PFS 2)。通过卡方检验比较患者特征结果:在多变量Cox模型中评估了预测PFS 2的因素结果:各组在年龄,阶段,年级和减量化方面具有可比性。预测PFS 2持续时间的多元模型包括组织学,分期,最佳减量化,PFS 1持续时间和类型作为未来研究的基准,G,A和U组合人群(去除V对延长PFS 2影响最大)的中位PFS 2为11.3个月(95%CI: 10.4-12.5个月)。PFS 2> PFS 1的患者百分比为14/90(16%)。在12个月时,仍有43%的患者无进展。建议将功效终点的简化基准用于cCR患者的未来合并试验。但是,建议的策略将需要在随机试验和更大的数据集中进行验证。

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