前列腺癌的预后和预测因素:历史观点和最近的国际共识倡议。

John R Srigley, Mahul Amin, Liliane Boccon-Gibod, Lars Egevad, Jonathan I Epstein, Peter A Humphrey, Gregor Mikuz, Don Newling, Sten Nilsson, Wael Sakr, Thomas M Wheeler, Rodolfo Montironi
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引用次数: 48

摘要

了解癌症医学中的预后对患者护理、研究和癌症控制项目都很重要。在前列腺癌中,考虑到这种疾病在临床、形态学和生物分子水平上的显著异质性,预后因素尤为重要。临床分期和组织学分级历来是界定前列腺癌异质性的主要因素。最近,血清前列腺特异性抗原测定被认为具有重要的预后作用。在过去的二十年里,对生物标志物的研究呈爆炸式增长,其中许多被认为具有预测意义。在本文中,我们概述了过去十几年来发生的各种共识倡议。评估预后因素和预测因素分类的标准已经出现,这些标准已被证明是有用的,并促进了我们对前列腺癌生物学的理解。这些共识倡议的结果为目前关于前列腺癌预后(预测)的国际磋商奠定了基础。我们对新的和有希望的预后因素的理解的进步将需要更严格的循证方法来分析已发表的研究。此外,还必须采用适当的数学模型来分析影响预报系统的多种因素。
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Prognostic and predictive factors in prostate cancer: historical perspectives and recent international consensus initiatives.

An understanding of prognosis in cancer medicine is important for patient care, research and cancer control programs. In prostate cancer, prognostic (predictive) factors are particularly important given the marked heterogeneity of this disease at clinical, morphologic and biomolecular levels. Clinical stage and histologic grade have historically played major roles in defining heterogeneity in prostate cancer. More recently, serum prostate-specific antigen measurement has assumed a significant prognostic role. Over the last two decades there has been an explosion of research into biomarkers, many of which have been purported to have prognostic significance. In this paper we present an overview of the various consensus initiatives that have transpired over the last dozen years. Criteria for evaluating prognostic factors and classifications of predictive factors have emerged that have proven useful and advanced our understanding of the biology of prostate cancer. The results of these consensus initiatives form a foundation on which the current international consultation on prognosis (prediction) in prostate cancer is built. Advances in our understanding of the new and promising prognostic factors will require a more rigorous evidence-based approach to the analysis of published studies. Furthermore, appropriate mathematical models for the analysis of the multiple factors that influence a prognostic system will have to be employed.

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