利用颅骨和中外侧斜位数字乳腺摄影与长期乳腺癌症风险的关联和预测。

Simin Chen, Rulla M Tamimi, Graham A Colditz, Shu Jiang
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摘要

乳腺体积密度百分比是癌症的一个重要危险因素。流行病学研究历来使用仅限于头尾侧(CC)视图的电影图像来估计基于区域的乳房密度。最近使用数字乳腺摄影图像的研究通常使用头尾侧(CC)和中侧斜侧(MLO)视图乳腺摄影之间的平均密度来预测5年和10年的风险。使用任一种和两种乳房X光检查视图的性能尚未得到很好的研究。我们使用Joanne Knight乳腺健康队列中的3804张全视野数字乳房X光片(294例发病病例和657例对照),来量化从任一和两种乳房X光摄影视图中提取的体积密度百分比之间的关联,并评估5年和10年乳腺癌症风险预测性能。我们的研究结果表明,CC、MLO的体积密度百分比与两者之间的平均值之间的相关性与癌症风险保持基本相同。5年期和10年期风险预测也显示出类似的预测准确性。因此,一种观点足以评估相关性并预测癌症在5年或10年内的未来风险。预防相关性:扩大数字乳腺摄影和重复筛查的使用为风险评估提供了机会。要使用这些图像进行风险估计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性能的贡献可以指导未来在日常护理中应用风险管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk.

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.

Prevention relevance: 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.

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