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一项病例对照研究中用于乳腺癌风险的乳腺X线纹理标志物的外部验证

External validation of a mammographic texture marker for breast cancer risk in a case-control study.

作者信息

Wang Chao, Brentnall Adam R, Mainprize James, Yaffe Martin, Cuzick Jack, Harvey Jennifer A

机构信息

Kingston University and St. George's, University of London, Faculty of Health, Social Care and Education, London, United Kingdom.

Queen Mary University of London, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Centre for Cancer Prevention, London, United Kingdom.

出版信息

J Med Imaging (Bellingham). 2020 Jan;7(1):014003. doi: 10.1117/1.JMI.7.1.014003. Epub 2020 Feb 12.

DOI:10.1117/1.JMI.7.1.014003
PMID:32064299
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7013151/
Abstract

: The pattern of dense tissue on a mammogram appears to provide additional information than overall density for risk assessment, but there has been little consistency in measures of texture identified. The purpose of this study is thus to validate a mammographic texture feature developed from a previous study in a new setting. : A case-control study (316 invasive cases and 1339 controls) of women in Virginia, USA was used to validate a mammographic texture feature (MMTEXT) derived in a independent previous study. Analysis of predictive ability was adjusted for age, demographic factors, questionnaire risk factors (combined through the Tyrer-Cuzick model), and optionally BI-RADS breast density. Odds ratios per interquartile range (IQ-OR) in controls were estimated. Subgroup analysis assessed heterogeneity by mode of cancer detection (94 not detected by mammography). : MMTEXT was not a significant risk factor at 0.05 level after adjusting for classical risk factors ( , 95%CI 0.92 to 1.46), nor after further adjustment for BI-RADS density ( , 95%CI 0.76 to 1.10). There was weak evidence that MMTEXT was more predictive for cancers that were not detected by mammography (unadjusted for density: , 95%CI 0.99 to 2.15 versus 1.03, 95%CI 0.79 to 1.35, Phet 0.10; adjusted for density: , 95%CI 0.70 to 1.77 versus 0.76, 95%CI 0.55 to 1.05, Phet 0.21). : MMTEXT is unlikely to be a useful imaging marker for invasive breast cancer risk assessment in women attending mammography screening. Future studies may benefit from a larger sample size to confirm this as well as developing and validating other measures of risk. This negative finding demonstrates the importance of external validation.

摘要

乳房X光片上致密组织的模式似乎比总体密度能提供更多用于风险评估的信息,但在已确定的纹理测量方面几乎没有一致性。因此,本研究的目的是在新环境中验证一项先前研究中开发的乳房X光片纹理特征。

在美国弗吉尼亚州对女性进行了一项病例对照研究(316例浸润性病例和1339例对照),以验证在先前一项独立研究中得出的乳房X光片纹理特征(MMTEXT)。对预测能力的分析针对年龄、人口统计学因素、问卷风险因素(通过泰勒-库齐克模型综合)以及可选的BI-RADS乳房密度进行了调整。估计了对照组中每四分位数间距的比值比(IQ-OR)。亚组分析通过癌症检测方式评估异质性(94例未通过乳房X光检查检测到)。

在调整经典风险因素后,MMTEXT在0.05水平上不是显著的风险因素( ,95%置信区间0.92至1.46),在进一步调整BI-RADS密度后也不是( ,95%置信区间0.76至1.10)。有微弱证据表明,MMTEXT对未通过乳房X光检查检测到的癌症更具预测性(未调整密度: ,95%置信区间0.99至2.15,而1.03,95%置信区间0.79至1.35,Phet 0.10;调整密度后: ,95%置信区间0.70至1.77,而0.76,95%置信区间0.55至1.05,Phet 0.21)。

MMTEXT不太可能成为接受乳房X光筛查的女性浸润性乳腺癌风险评估的有用影像标志物。未来的研究可能会受益于更大的样本量来证实这一点,以及开发和验证其他风险测量方法。这一阴性结果证明了外部验证的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c5a/7013151/1f4f8a23fb31/JMI-007-014003-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c5a/7013151/9f90d2827ad0/JMI-007-014003-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c5a/7013151/4a0604ef8781/JMI-007-014003-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c5a/7013151/1f4f8a23fb31/JMI-007-014003-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c5a/7013151/9f90d2827ad0/JMI-007-014003-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c5a/7013151/4a0604ef8781/JMI-007-014003-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c5a/7013151/1f4f8a23fb31/JMI-007-014003-g003.jpg

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本文引用的文献

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J Breast Imaging. 2019 Jun;1(2):99-106. doi: 10.1093/jbi/wbz006. Epub 2019 May 11.
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Long-term Accuracy of Breast Cancer Risk Assessment Combining Classic Risk Factors and Breast Density.经典风险因素与乳腺密度相结合的乳腺癌风险评估的长期准确性。
JAMA Oncol. 2018 Sep 1;4(9):e180174. doi: 10.1001/jamaoncol.2018.0174. Epub 2018 Sep 13.
3
A comparison of five methods of measuring mammographic density: a case-control study.
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4
A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies.一种新颖且完全自动化的乳腺 X 线摄影纹理分析用于风险预测:来自两项病例对照研究的结果。
Breast Cancer Res. 2017 Oct 18;19(1):114. doi: 10.1186/s13058-017-0906-6.
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Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms.深度学习在乳腺癌风险评估中的应用:基于全场数字化乳腺X线摄影临床数据集对卷积神经网络的评估
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