Cancer Research UK Oxford Centre, University of Oxford, UK; Nuffield Department of Primary Care Health Sciences, University of Oxford, UK.
Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, UK.
Lancet Digit Health. 2023 Sep;5(9):e571-e581. doi: 10.1016/S2589-7500(23)00113-9.
Identifying female individuals at highest risk of developing life-threatening breast cancers could inform novel stratified early detection and prevention strategies to reduce breast cancer mortality, rather than only considering cancer incidence. We aimed to develop a prognostic model that accurately predicts the 10-year risk of breast cancer mortality in female individuals without breast cancer at baseline.
In this model development and validation study, we used an open cohort study from the QResearch primary care database, which was linked to secondary care and national cancer and mortality registers in England, UK. The data extracted were from female individuals aged 20-90 years without previous breast cancer or ductal carcinoma in situ who entered the cohort between Jan 1, 2000, and Dec 31, 2020. The primary outcome was breast cancer-related death, which was assessed in the full dataset. Cox proportional hazards, competing risks regression, XGBoost, and neural network modelling approaches were used to predict the risk of breast cancer death within 10 years using routinely collected health-care data. Death due to causes other than breast cancer was the competing risk. Internal-external validation was used to evaluate prognostic model performance (using Harrell's C, calibration slope, and calibration in the large), performance heterogeneity, and transportability. Internal-external validation involved dataset partitioning by time period and geographical region. Decision curve analysis was used to assess clinical utility.
We identified data for 11 626 969 female individuals, with 70 095 574 person-years of follow-up. There were 142 712 (1·2%) diagnoses of breast cancer, 24 043 (0·2%) breast cancer-related deaths, and 696 106 (6·0%) deaths from other causes. Meta-analysis pooled estimates of Harrell's C were highest for the competing risks model (0·932, 95% CI 0·917-0·946). The competing risks model was well calibrated overall (slope 1·011, 95% CI 0·978-1·044), and across different ethnic groups. Decision curve analysis suggested favourable clinical utility across all age groups. The XGBoost and neural network models had variable performance across age and ethnic groups.
A model that predicts the combined risk of developing and then dying from breast cancer at the population level could inform stratified screening or chemoprevention strategies. Further evaluation of the competing risks model should comprise effect and health economic assessment of model-informed strategies.
Cancer Research UK.
确定罹患危及生命的乳腺癌风险最高的女性个体，有助于制定新的分层早期检测和预防策略，以降低乳腺癌死亡率，而不仅仅考虑癌症发病率。我们旨在开发一种预后模型，该模型能够准确预测基线时无乳腺癌的女性个体在 10 年内的乳腺癌死亡风险。
在这项模型开发和验证研究中，我们使用了来自英国 QResearch 初级保健数据库的开放队列研究，该数据库与二级保健以及国家癌症和死亡率登记处相关联。提取的数据来自年龄在 20-90 岁之间、无既往乳腺癌或导管原位癌且在 2000 年 1 月 1 日至 2020 年 12 月 31 日期间入组队列的女性个体。主要结局是乳腺癌相关死亡，在全数据集进行评估。使用常规收集的医疗保健数据，采用 Cox 比例风险、竞争风险回归、XGBoost 和神经网络建模方法预测 10 年内乳腺癌死亡的风险。乳腺癌以外的其他原因导致的死亡为竞争风险。内部-外部验证用于评估预后模型的性能（使用 Harrell 的 C、校准斜率和大样本校准）、性能异质性和可转移性。内部-外部验证包括按时间和地理区域划分数据集。决策曲线分析用于评估临床实用性。
我们确定了 11626969 名女性个体的数据，随访时间为 70095574 人年。有 142712（1.2%）例乳腺癌诊断，24043（0.2%）例乳腺癌相关死亡，696106（6.0%）例其他原因死亡。荟萃分析中，竞争风险模型的 Harrell 的 C 估计值最高（0.932，95%CI 0.917-0.946）。总体而言，竞争风险模型校准良好（斜率 1.011，95%CI 0.978-1.044），并且在不同种族群体中也是如此。决策曲线分析表明，在所有年龄组中均具有良好的临床实用性。XGBoost 和神经网络模型在年龄和种族群体之间的性能存在差异。
能够预测人群中罹患乳腺癌和死于乳腺癌的综合风险的模型，可以为分层筛查或化学预防策略提供信息。应进一步评估竞争风险模型，包括对模型指导策略的效果和健康经济学评估。
英国癌症研究基金会。
