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乳腺癌风险预测模型的独立验证，对于合理使用至关重要，并且可为筛查和预防的选择提供决策依据。对常用乳腺癌风险预测模型进行独立验证的队列研究不多，而且已经完成的队列研究样本量较小、随访时间较短、预测工具版本较老。 2019年2月21日，英国《柳叶刀》肿瘤学分册在线发表美国哥伦比亚大学、斯坦福大学、犹他大学、费城癌症中心、澳大利亚墨尔本大学、莫纳什大学、维多利亚州癌症协会、圣文森特医院、新南威尔士大学、威尔斯亲王医院、彼得麦卡伦癌症中心、加拿大多伦多西奈山医院、多伦多大学的研究报告，验证了四种常用乳腺癌风险模型的相对效果，并且评定了有限数据输入对各自效果的影响。 该验证研究从1992年3月17日～2011年6月29日入组时无乳腺癌的1万8856例澳大利亚、加拿大、美国女性乳腺癌前瞻家族研究队列（ProF-SC）选择了年龄为20～70岁并且既往无双侧预防性乳房切除术或卵巢癌史、至少有2个月随访数据并且有乳腺癌家族史信息的1万5732例女性，计算并比较四种乳腺癌风险预测模型的10年风险评分：
根据该队列预测乳腺癌病例数与实际乳腺癌病例数之比，对模型校准度进行比较，并且通过10年之内是否将被诊断为乳腺癌的一致性统计，对模型区分度进行比较。通过亚组分析，比较BRCA1或BRCA2突变携带者（即BRCA阳性女性）、检测非携带者和未检测参与者（即BRCA阴性女性）、入组时年龄小于50岁参与者的模型效果。并且评定有限数据输入（例如，家族史和非基因信息的数量限制）对模型效果的影响。 结果，该队列研究选出的1万5732例女性入组后，中位随访11.1年（四分位：6.0～14.4）期间，实际发生乳腺癌619例（4%），其中组织学证实519例（84%）。 对于整体验证队列，BOADICEA和IBIS的预测病例数接近实际病例数，而BRCAPRO和BCRAT的预测病例数低于实际病例数，预测病例数与实际病例数之比及其95%置信区间：
对于整体验证队列，10年之内是否将被诊断为乳腺癌的一致性及其95%置信区间：
根据BRCA突变状态亚组分析，对于BRCA阴性女性，BOADICEA和IBIS的预测病例数接近实际病例数，而BRCAPRO和BCRAT的预测病例数低于实际病例数，预测病例数与实际病例数之比及其95%置信区间：
对于BRCA阳性参与者，BOADICEA和IBIS的预测病例数接近实际病例数，而BRCAPRO的预测病例数低于实际病例数，预测病例数与实际病例数之比及其95%置信区间：
对于入组时年龄小于50岁的女性，BOADICEA和IBIS的预测病例数接近实际病例数，而BRCAPRO和BCRAT的预测病例数低于实际病例数，预测病例数与实际病例数之比及其95%置信区间：
对于一级和二级亲属的家族史输入数据限制，BOADICEA和IBIS的预测评分未受明显影响。 因此，该研究结果表明，BOADICEA和IBIS等考虑多代家族史的模型，预测乳腺癌风险的能力较强，即使对于平均或低于平均乳腺癌风险的女性。虽然BOADICEA和IBIS的效果相似，但是如果将BOADICEA的多基因风险因素、IBIS的非家族史风险因素结合在一起，该混合模型可能进一步提高预测精准性。 对此，美国国家卫生研究院国家癌症研究所、霍普金斯大学的癌症流行病学和遗传学专家发表同期评论：乳腺癌发生风险预测工具哪家强？ Lancet Oncol. 2019 Feb 21. [Epub ahead of print] 10-year performance of four models of breast cancer risk: a validation study. Mary Beth Terry, Yuyan Liao, Alice S Whittemore, Nicole Leoce, Richard Buchsbaum, Nur Zeinomar, Gillian S Dite, Wendy K Chung, Julia A Knight, Melissa C Southey, Roger L Milne, David Goldgar, Graham G Giles, Sue-Anne McLachlan, Michael L Friedlander, Prue C Weideman, Gord Glendon, Stephanie Nesci, Irene L Andrulis, Esther M John, Kelly-Anne Phillips, Mary B Daly, Saundra S Buys, John L Hopper, Robert J MacInnis. Columbia University, New York, NY, USA; Columbia University Medical Center, New York, NY, USA; Stanford University School of Medicine, Stanford, CA, USA; University of Melbourne, Parkville, VIC, Australia; Sinai Health System, Toronto, ON, Canada; University of Toronto, Toronto, ON, Canada; Monash University, Clayton, VIC, Australia; Cancer Council Victoria, Melbourne, VIC, Australia; University of Utah Health, Salt Lake City, UT, USA; St Vincent's Hospital, Parkville, VIC, Australia; University of New South Wales, Sydney, NSW, Australia; Prince of Wales Hospital, NSW, Australia; Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Fox Chase Cancer Center, Philadelphia, PA, USA. BACKGROUND: Independent validation is essential to justify use of models of breast cancer risk prediction and inform decisions about prevention options and screening. Few independent validations had been done using cohorts for common breast cancer risk prediction models, and those that have been done had small sample sizes and short follow-up periods, and used earlier versions of the prediction tools. We aimed to validate the relative performance of four commonly used models of breast cancer risk and assess the effect of limited data input on each one's performance. METHODS: In this validation study, we used the Breast Cancer Prospective Family Study Cohort (ProF-SC), which includes 18856 women from Australia, Canada, and the USA who did not have breast cancer at recruitment, between March 17, 1992, and June 29, 2011. We selected women from the cohort who were 20-70 years old and had no previous history of bilateral prophylactic mastectomy or ovarian cancer, at least 2 months of follow-up data, and information available about family history of breast cancer. We used this selected cohort to calculate 10-year risk scores and compare four models of breast cancer risk prediction: the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm model (BOADICEA), BRCAPRO, the Breast Cancer Risk Assessment Tool (BCRAT), and the International Breast Cancer Intervention Study model (IBIS). We compared model calibration based on the ratio of the expected number of breast cancer cases to the observed number of breast cancer cases in the cohort, and on the basis of their discriminatory ability to separate those who will and will not have breast cancer diagnosed within 10 years as measured with the concordance statistic (C-statistic). We did subgroup analyses to compare the performance of the models at 10 years in BRCA1 or BRCA2 mutation carriers (ie, BRCA-positive women), tested non-carriers and untested participants (ie, BRCA-negative women), and participants younger than 50 years at recruitment. We also assessed the effect that limited data input (eg, restriction of the amount of family history and non-genetic information included) had on the models' performance. FINDINGS: After median follow-up of 11.1 years (IQR 6.0-14.4), 619 (4%) of 15732 women selected from the ProF-SC cohort study were prospectively diagnosed with breast cancer after recruitment, of whom 519 (84%) had histologically confirmed disease. BOADICEA and IBIS were well calibrated in the overall validation cohort, whereas BRCAPRO and BCRAT underpredicted risk (ratio of expected cases to observed cases 1.05 [95% CI 0.97-1.14] for BOADICEA, 1.03 [0.96-1.12] for IBIS, 0.59 [0.55-0.64] for BRCAPRO, and 0.79 [0.73-0.85] for BRCAT). The estimated C-statistics for the complete validation cohort were 0.70 (95% CI 0.68-0.72) for BOADICEA, 0.71 (0.69-0.73) for IBIS, 0.68 (0.65-0.70) for BRCAPRO, and 0.60 (0.58-0.62) for BCRAT. In subgroup analyses by BRCA mutation status, the ratio of expected to observed cases for BRCA-negative women was 1.02 (95% CI 0.93-1.12) for BOADICEA, 1.00 (0.92-1.10) for IBIS, 0.53 (0.49-0.58) for BRCAPRO, and 0.97 (0.89-1.06) for BCRAT. For BRCA-positive participants, BOADICEA and IBIS were well calibrated, but BRCAPRO underpredicted risk (ratio of expected to observed cases 1.17 [95% CI 0.99-1.38] for BOADICEA, 1.14 [0.96-1.35] for IBIS, and 0.80 [0.68-0.95] for BRCAPRO). We noted similar patterns of calibration for women younger than 50 years at recruitment. Finally, BOADICEA and IBIS predictive scores were not appreciably affected by limiting input data to family history for first-degree and second-degree relatives. INTERPRETATION: Our results suggest that models that include multigenerational family history, such as BOADICEA and IBIS, have better ability to predict breast cancer risk, even for women at average or below-average risk of breast cancer. Although BOADICEA and IBIS performed similarly, further improvements in the accuracy of predictions could be possible with hybrid models that incorporate the polygenic risk component of BOADICEA and the non-family-history risk factors included in IBIS. FUNDING: US National Institutes of Health, National Cancer Institute, Breast Cancer Research Foundation, Australian National Health and Medical Research Council, Victorian Health Promotion Foundation, Victorian Breast Cancer Research Consortium, Cancer Australia, National Breast Cancer Foundation, Queensland Cancer Fund, Cancer Councils of New South Wales, Victoria, Tasmania, and South Australia, and Cancer Foundation of Western Australia. DOI: 10.1016/S1470-2045(18)30902-1 Lancet Oncol. 2019 Feb 21. [Epub ahead of print] Assessment of breast cancer risk: which tools to use? Montserrat Garcia-Closas, Nilanjan Chatterjee. National Cancer Institute, Shady Grove Campus, Rockville, MD; Johns Hopkins University, Baltimore, MD, USA. DOI: 10.1016/S1470-2045(19)30071-3
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