Population Health Department, Queensland Institute of Medical Research, Queensland, Australia.
Cancer Epidemiol. 2013 Aug;37(4):349-52. doi: 10.1016/j.canep.2013.04.002. Epub 2013 May 1.
Growing awareness of the potential to predict a person's future risk of cancer has resulted in the development of numerous algorithms. Such algorithms aim to improve the ability of policy makers, doctors and patients to make rational decisions about behaviour modification or surveillance, with the expectation that this activity will lead to overall benefit. There remains debate however, about whether accurate risk prediction is achievable for most cancers.
We conducted a brief narrative review of the literature regarding the history and challenges of risk prediction, highlighting our own recent experiences in developing tools for oesophageal adenocarcinoma.
While tools for predicting future risk of cardiovascular outcomes have been translated successfully to clinical practice, the experience with cancer risk prediction has been mixed. Models have now been developed and validated for predicting risk of melanoma and cancers of the breast, colo-rectum, lung, liver, oesophagus and prostate, and while several of these have adequate performance at the population-level, none to date have adequate discrimination for predicting risk in individual patients. Challenges of individual risk prediction for cancer are many, and include long latency, multiple risk factors of mostly small effect, and incomplete knowledge of the causal pathways.
人们越来越意识到预测一个人未来癌症风险的潜力，这导致了许多算法的发展。这些算法旨在提高决策者、医生和患者在行为改变或监测方面做出合理决策的能力，期望这种活动能带来整体效益。然而，对于大多数癌症来说，是否能实现准确的风险预测仍存在争议。
我们对文献进行了简短的叙述性综述，探讨了风险预测的历史和挑战，重点介绍了我们自己在开发食管腺癌工具方面的最新经验。
虽然预测心血管结局未来风险的工具已成功转化为临床实践，但癌症风险预测的经验却喜忧参半。目前已经开发和验证了用于预测黑色素瘤和乳腺癌、结直肠癌、肺癌、肝癌、食管癌和前列腺癌风险的模型，尽管其中一些在人群水平上具有足够的性能，但迄今为止，没有一个模型能够充分区分个体患者的风险。癌症个体风险预测面临着许多挑战，包括潜伏期长、多种风险因素大多影响较小，以及对因果途径的了解不完整。
