个体化乳腺癌风险预测模型哪家强

　　个体化乳腺癌风险预测模型可能是根据风险对筛查方法进行规划的关键。

　　2019年5月22日，英国癌症研究基金会、英国《自然》旗下《英国癌症杂志》在线发表西班牙巴塞罗那自治大学、地中海医院医学研究所、慢性疾病医疗服务研究网络、英国格拉斯哥大学的研究报告，对普通女性人群个体化乳腺癌风险预测模型进行了系统回顾和质量评定。

　　该研究按照考科蓝合作组织的方法，对Medline、EMBASE、Cochrane图书馆数据库截至2018年2月发表的一般女性人群个体化乳腺癌风险推算模型研究进行检索，由两位独立评审者对研究质量进行评定，对于结果进行汇总。

　　结果，从检索出的2976篇文献筛选出24项研究。其中，20项研究针对四个模型：

• 乳腺癌风险评定工具（BCRAT）

• 乳腺癌监测联盟（BCSC）

• 罗斯纳与科尔迪茨模型（哈佛大医学院和公共卫生学院、布莱根医院和波士顿妇女医院发起的护士健康研究）

• 国际乳腺癌干预研究（IBIS）

　　其他四项研究针对其他原创模型。其中四项研究包括遗传信息。所有研究质量中等，辨别力和数据录入存在一些局限性。对于乳腺癌筛查，这些研究报告的受试者操作特征曲线下面积最大值为0.71（2017年瑞典卡罗林医学院发表的乳腺癌短期风险临床模型，不过未经外部验证）。

　　因此，个体化风险预测模型有望成为根据风险实施筛查政策的有力工具。不过，由于这些模型需要进一步提高质量和辨别能力，故推荐其中任何一个模型都很困难。

Br J Cancer. 2019 May 22. [Epub ahead of print]

A systematic review and quality assessment of individualised breast cancer risk prediction models.

Javier Louro, Margarita Posso, Michele Hilton Boon, Marta Román, Laia Domingo, Xavier Castells, María Sala.

Hospital del Mar Medical Research Institute, Barcelona, Spain; Research Network on Health Services in Chronic Diseases, Barcelona, Spain; Universitat Autónoma de Barcelona, Barcelona, Spain; University of Glasgow, Glasgow, UK.

BACKGROUND: Individualised breast cancer risk prediction models may be key for planning risk-based screening approaches. Our aim was to conduct a systematic review and quality assessment of these models addressed to women in the general population.

METHODS: We followed the Cochrane Collaboration methods searching in Medline, EMBASE and The Cochrane Library databases up to February 2018. We included studies reporting a model to estimate the individualised risk of breast cancer in women in the general population. Study quality was assessed by two independent reviewers. Results are narratively summarised.

RESULTS: We included 24 studies out of the 2976 citations initially retrieved. Twenty studies were based on four models, the Breast Cancer Risk Assessment Tool (BCRAT), the Breast Cancer Surveillance Consortium (BCSC), the Rosner & Colditz model, and the International Breast Cancer Intervention Study (IBIS), whereas four studies addressed other original models. Four of the studies included genetic information. The quality of the studies was moderate with some limitations in the discriminative power and data inputs. A maximum AUROC value of 0.71 was reported in the study conducted in a screening context.

CONCLUSION: Individualised risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity.

DOI: 10.1038/s41416-019-0476-8