前列腺癌中的镥:从已发表的试验中重建患者水平数据并生成多试验Kaplan-Meier曲线。

Andrea Messori
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引用次数: 3

摘要

背景:在转移性去势抵抗性前列腺癌的预治疗中,镥已被证明是一个重要的潜在创新。迄今为止,已有两项临床试验对镥进行了评估(分别对99名和385名患者进行了治疗和视力评估),但结果并不一致。目的:综合现有证据,探讨镥治疗转移性去势抵抗性前列腺癌的疗效;并测试一种新的人工智能技术的应用,该技术基于重建的患者级数据综合有效性。方法:我们采用一种新的人工智能方法(shiny method)对这两项试验的生存数据进行汇总,并评估镥组之间的差异程度。这种闪亮的技术采用了Kaplan-Meier曲线中单个患者数据的原始重建。分析和比较两个镥组的无进展生存图。结果:估计的风险比支持视力试验;差异有统计学意义(P < 0.001)。这些结果表明需要对镥进行进一步的研究,因为迄今为止发表的两项试验的生存数据是相互矛盾的。结论:我们的研究证实了从生存图中重建患者水平数据以产生生存统计数据的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Lutetium in prostate cancer: Reconstruction of patient-level data from published trials and generation of a multi-trial Kaplan-Meier curve.

Lutetium in prostate cancer: Reconstruction of patient-level data from published trials and generation of a multi-trial Kaplan-Meier curve.

Background: Lutetium has been shown to be an important potential innovation in pre-treated metastatic castration-resistant prostate cancer. Two clinical trials have evaluated lutetium thus far (therap and vision with 99 and 385 patients, respectively), but their results are discordant.

Aim: To synthetize the available evidence on the effectiveness of lutetium in pre-treated metastatic castration-resistant prostate cancer; and to test the application of a new artificial intelligence technique that synthetizes effectiveness based on reconstructed patient-level data.

Methods: We employed a new artificial intelligence method (shiny method) to pool the survival data of these two trials and evaluate to what extent the lutetium cohorts differed from one another. The shiny technique employs an original reconstruction of individual patient data from the Kaplan-Meier curves. The progression-free survival graphs of the two lutetium cohorts were analyzed and compared.

Results: The hazard ratio estimated was in favor of the vision trial; the difference was statistically significant (P < 0.001). These results indicate that further studies on lutetium are needed because the survival data of the two trials published thus far are conflicting.

Conclusion: Our study confirms the feasibility of reconstructing patient-level data from survival graphs in order to generate a survival statistics.

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