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乳腺癌淋巴结转移的随机效应模型:使用连续增长模型量化协变量和筛查的作用。

Random effects models of lymph node metastases in breast cancer: quantifying the roles of covariates and screening using a continuous growth model.

机构信息

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

出版信息

Biometrics. 2022 Mar;78(1):376-387. doi: 10.1111/biom.13430. Epub 2021 Feb 7.

DOI:10.1111/biom.13430
PMID:33501643
Abstract

We recently described a joint model of breast cancer tumor size and number of affected lymph nodes, which conditions on screening history, mammographic density, and mode of detection, and can be used to infer growth rates, time to symptomatic detection, screening sensitivity, and rates of lymph node spread. The model of lymph node spread can be estimated in isolation from measurements of tumor volume and number of affected lymph nodes, giving inference identical to the joint model. Here, we extend our model to include covariate effects. We also derive theoretical results in order to study the role of screening on lymph node metastases at diagnosis. We analyze the association between hormone replacement therapy (HRT) and breast cancer lymph node spread, using data from a case-control study designed specifically to study the effects of HRT on breast cancer. Using our method, we estimate that women using HRT at time of diagnosis have a 36% lower rate of lymph node spread than nonusers (95% confidence interval [CI] =(8%,58%)). This can be contrasted with the effect of HRT on the tumor growth rate, estimated here to be 15% slower in HRT users (95% CI = (-34%,+7%)). For screen-detected cancers, we illustrate how lead time can relate to lymph node spread; and using symptomatic cancers, we illustrate the potential consequences of false negative screens in terms of lymph node spread.

摘要

我们最近描述了一个联合模型,用于预测乳腺癌肿瘤大小和受累淋巴结数量,该模型考虑了筛查史、乳腺密度和检测方式等因素,可以用于推断生长速度、症状出现前的时间、筛查敏感性和淋巴结转移率。该模型可以在不考虑肿瘤体积和受累淋巴结数量的情况下单独估计淋巴结转移情况,其推断结果与联合模型相同。在这里,我们将模型扩展到包括协变量效应。我们还推导出了一些理论结果,以研究筛查对诊断时淋巴结转移的作用。我们使用一项专门用于研究激素替代疗法 (HRT) 对乳腺癌影响的病例对照研究的数据,分析了激素替代疗法 (HRT) 与乳腺癌淋巴结转移之间的关系。使用我们的方法,我们估计在诊断时使用 HRT 的女性淋巴结转移率比未使用者低 36%(95%置信区间 [CI] =(8%,58%))。这与 HRT 对肿瘤生长速度的影响形成对比,在这里估计 HRT 使用者的肿瘤生长速度慢 15%(95% CI =(-34%,+7%))。对于筛查发现的癌症,我们说明了如何将领先时间与淋巴结转移联系起来;并使用症状性癌症,说明了假阴性筛查在淋巴结转移方面的潜在后果。

相似文献

1
Random effects models of lymph node metastases in breast cancer: quantifying the roles of covariates and screening using a continuous growth model.乳腺癌淋巴结转移的随机效应模型:使用连续增长模型量化协变量和筛查的作用。
Biometrics. 2022 Mar;78(1):376-387. doi: 10.1111/biom.13430. Epub 2021 Feb 7.
2
Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.存在筛查的情况下,对新发生乳腺癌病例的肿瘤大小和淋巴结转移的联合模型。
Stat Methods Med Res. 2019 Dec;28(12):3822-3842. doi: 10.1177/0962280218819568. Epub 2019 Jan 3.
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Lymph node metastases in breast cancer: Investigating associations with tumor characteristics, molecular subtypes and polygenic risk score using a continuous growth model.乳腺癌淋巴结转移:应用连续生长模型研究肿瘤特征、分子亚型和多基因风险评分与淋巴结转移的相关性。
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Hormone replacement therapy and mammographic screening outcomes in Western Australia.西澳大利亚州的激素替代疗法与乳房X光筛查结果
J Med Screen. 2006;13(2):93-7. doi: 10.1258/096914106777589588.
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Hormone replacement therapy, percent mammographic density, and sensitivity of mammography.激素替代疗法、乳腺X线摄影密度百分比以及乳腺X线摄影的敏感性。
Cancer Epidemiol Biomarkers Prev. 2005 May;14(5):1060-4. doi: 10.1158/1055-9965.EPI-04-0165.
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Anticancer Res. 2016 Jan;36(1):393-7.

引用本文的文献

1
The association between lymph node status and the tumor size in breast cancer - results from the Danish Breast Cancer Group (DBCG).乳腺癌中淋巴结状态与肿瘤大小的关联——丹麦乳腺癌协作组(DBCG)的研究结果
Acta Oncol. 2025 Aug 5;64:1021-1028. doi: 10.2340/1651-226X.2025.43380.
2
Tumour Growth Models of Breast Cancer for Evaluating Early Detection-A Summary and a Simulation Study.用于评估早期检测的乳腺癌肿瘤生长模型——综述与模拟研究
Cancers (Basel). 2023 Jan 31;15(3):912. doi: 10.3390/cancers15030912.
3
A natural history and copula-based joint model for regional and distant breast cancer metastasis.
基于自然史和连接函数的局部和远处乳腺癌转移联合模型。
Stat Methods Med Res. 2022 Dec;31(12):2415-2430. doi: 10.1177/09622802221122410. Epub 2022 Sep 18.