Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk.
Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk.
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DOI：
10.1158/1940-6207.capr-22-0499
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发表时间：
2023-09-01
影响因子：
3.3
通讯作者：
Jiang, Shu
中科院分区：
文献类型：
--
作者：
Chen, Simin;Tamimi, Rulla M.;Colditz, Graham A.;Jiang, Shu
数字乳房X光检查和重复筛查的扩大使用为风险评估提供了机会。要使用这些图像进行风险评估并实时指导风险管理，需要进行有效的处理。评估不同观点对预测绩效的贡献可以指导未来常规护理中风险管理的应用。乳腺X线摄影的体积密度百分比是乳腺癌的重要危险因素。流行病学研究历来使用胶片图像来估计基于区域的乳房密度，通常仅限于头尾(CC)切面。最近使用数字乳房摄影图像的研究通常使用头尾(CC)和内侧斜位(MLO)乳房摄影之间的平均密度来预测5年和10年的风险。使用任何一个和两个乳房X光照片的性能还没有得到很好的调查。我们使用来自Joanne Knight Breast Health Cohort的3,804张全视野数字乳房X光照片(294个事件病例和657个对照)，来量化从任何一个乳房X光检查视图和两个乳房X光检查视图提取的体积密度百分比之间的关联，并评估5年和10年乳腺癌风险预测性能。我们的结果表明，来自CC、MLO的百分比体积密度和两者之间的平均值之间的关联基本上与乳腺癌风险保持相同的关联。5年和10年的风险预测也显示出类似的预测精度。因此，一种观点足以评估相关性并预测未来5年或10年内乳腺癌的风险。数字乳房X光检查和重复筛查的扩大使用为风险评估提供了机会。要使用这些图像进行风险评估并实时指导风险管理，需要进行有效的处理。评估不同观点对预测绩效的贡献可以指导未来常规护理中风险管理的应用。
Expanding use of digital mammography and repeated screening provides opportunities for risk assessment. To use these images for risk estimates and guide risk management in real time requires efficient processing. Evaluating the contribution of different views to prediction performance can guide future applications for risk management in routine care. Mammographic percentage of volumetric density is an important risk factor for breast cancer. Epidemiology studies historically used film images often limited to craniocaudal (CC) views to estimate area-based breast density. More recent studies using digital mammography images typically use the averaged density between craniocaudal (CC) and mediolateral oblique (MLO) view mammography for 5- and 10-year risk prediction. The performance in using either and both mammogram views has not been well-investigated. We use 3,804 full-field digital mammograms from the Joanne Knight Breast Health Cohort (294 incident cases and 657 controls), to quantity the association between volumetric percentage of density extracted from either and both mammography views and to assess the 5 and 10-year breast cancer risk prediction performance. Our results show that the association between percent volumetric density from CC, MLO, and the average between the two, retain essentially the same association with breast cancer risk. The 5- and 10-year risk prediction also shows similar prediction accuracy. Thus, one view is sufficient to assess association and predict future risk of breast cancer over a 5 or 10-year interval. Expanding use of digital mammography and repeated screening provides opportunities for risk assessment. To use these images for risk estimates and guide risk management in real time requires efficient processing. Evaluating the contribution of different views to prediction performance can guide future applications for risk management in routine care.