Department of Population Health, QIMR Berghofer Medical Research Institute, Queensland, Australia.
School of Public Health, University of Queensland, Queensland, Australia.
J Natl Cancer Inst. 2018 Oct 1;110(10):1075-1083. doi: 10.1093/jnci/djy023.
Risk stratification can improve the efficacy and cost-efficiency of screening programs for early detection of cancer. We sought to derive a risk stratification tool for melanoma that was suitable for the general population using only self-reported information.
We used melanoma risk factor information collected at baseline from QSKIN, a prospective cohort study of Queensland adults age 40 to 69 years at recruitment (n = 41 954). We examined two separate outcomes: 1) invasive melanomas and 2) all melanomas (invasive + in situ) obtained through data linkage to the cancer registry. We used stepwise Cox proportional hazards modeling to derive the risk models in a randomly selected two-thirds sample of the data set and assessed model performance in the remaining one-third validation sample.
After a median follow-up of 3.4 years, 655 (1.6%) participants developed melanoma (257 invasive, 398 in situ). The prediction model for invasive melanoma included seven terms. At baseline, the strongest predictors of invasive melanoma were age, sex, tanning ability, number of moles at age 21 years, and number of skin lesions treated destructively. The model for "all melanomas" (ie, invasive and in situ) included five additional terms. Discrimination in the validation data set was high for both models (C-index = 0.69, 95% confidence interval [CI] = 0.62 to 0.76, and C-index = 0.72, 95% CI = 0.69 to 0.75, respectively), and calibration was acceptable.
Such a tool could be used to target surveillance activities to those at highest predicted risk of developing melanoma over a median duration of 3.4 years.
风险分层可以提高癌症早期检测筛查计划的效果和成本效益。我们试图仅使用自我报告信息为普通人群开发一种适用于黑色素瘤的风险分层工具。
我们使用 QSKIN 前瞻性队列研究中在基线时收集的黑色素瘤危险因素信息，该研究纳入了昆士兰州招募时年龄在 40 至 69 岁的成年人（n = 41954）。我们检查了两个独立的结果：1）侵袭性黑色素瘤和 2）通过与癌症登记处的数据链接获得的所有黑色素瘤（侵袭性+原位）。我们使用逐步 Cox 比例风险建模在数据集的三分之二随机选择的样本中推导风险模型，并在剩余的三分之一验证样本中评估模型性能。
中位随访 3.4 年后，655 名（1.6%）参与者发生了黑色素瘤（257 例侵袭性，398 例原位）。侵袭性黑色素瘤预测模型包括七个术语。在基线时，侵袭性黑色素瘤最强的预测因素是年龄、性别、晒黑能力、21 岁时的痣数和破坏性治疗的皮肤病变数。“所有黑色素瘤”（即侵袭性和原位）模型包括另外五个术语。两个模型在验证数据集中的区分度都很高（C 指数=0.69，95%置信区间[CI]为 0.62 至 0.76，和 C 指数=0.72，95%CI 为 0.69 至 0.75），校准效果也可以接受。
这种工具可以用于针对那些在中位 3.4 年时间内黑色素瘤发生风险最高的人进行监测活动。
