Woman Imaging Division, Department of Radiology, Faculty of Universitas Pelita Harapan, Tangerang, Indonesia; Department of Radiology of Siloam Hospital MRCCC, Jakarta, Indonesia.
Woman Imaging Division, Department of Radiology, Faculty of Universitas Pelita Harapan, Tangerang, Indonesia; Department of Radiology of Siloam Hospital MRCCC, Jakarta, Indonesia.
Radiography (Lond). 2024 Aug;30(5):1455-1467. doi: 10.1016/j.radi.2024.08.003. Epub 2024 Aug 19.
Breast cancer is the most common cancer in women and a leading cause of mortality. This systematic review and meta-analysis aims to evaluate the correlation between breast density measurements obtained from various software and visual assessments by radiologists using full-field digital mammography (FFDM).
Following the PRISMA 2020 guidelines, five databases (Pubmed, Google Scholar, Science Direct, Cochrane Library, and MEDLINE) were searched for studies correlating volumetric breast density with breast cancer risk. The Newcastle-Ottawa Scale and the Joanna Briggs Institute Checklist were used to assess the quality of the included studies. Meta-analysis of correlation was applied to aggregate correlation coefficients using a random-effects model using MedCalc Statistical Software version 19.2.6.
The review included 22 studies with a total of 58,491 women. The pooled correlation coefficient for volumetric breast density amongst Volpara™ and Quantra™ was found to be 0.755 (95% CI 0.496-0.890, p < 0.001), indicating a high positive correlation, albeit with a significant heterogeneity (I = 99.89%, p < 0.0001). Subgroup analyses based on study origin, quality, and methodology were performed but did not reveal the heterogeneity cause. Egger's and Begg's tests showed no significant publication bias.
Volumetric breast density is strongly correlated with breast cancer risk, underscoring the importance of accurate breast density assessment in screening programs. Automated volumetric measurement tools like Volpara™ and Quantra™ provide reliable assessments, potentially improving breast cancer risk prediction and management.
Implementing fully automated breast density assessment tools could enhance consistency in clinical practice, minimizing observer variability and improving screening accuracy. These tools should be further validated against standardized criteria to ensure reliability in diverse clinical settings.
乳腺癌是女性最常见的癌症，也是导致死亡的主要原因。本系统评价和荟萃分析旨在评估使用全数字化乳腺摄影（FFDM）的各种软件和放射科医生的视觉评估获得的乳腺密度测量值之间的相关性。
根据 PRISMA 2020 指南，在 Pubmed、Google Scholar、Science Direct、Cochrane Library 和 MEDLINE 五个数据库中搜索了与体积乳腺密度与乳腺癌风险相关的研究。使用纽卡斯尔-渥太华量表和 Joanna Briggs 研究所清单评估纳入研究的质量。使用 MedCalc 统计软件版本 19.2.6 应用荟萃分析相关性，使用随机效应模型汇总相关系数。
该综述纳入了 22 项研究，共 58491 名女性。发现 Volpara™ 和 Quantra™ 之间的体积乳腺密度的汇总相关系数为 0.755（95% CI 0.496-0.890，p<0.001），表明存在高度正相关，但存在显著异质性（I=99.89%，p<0.0001）。根据研究来源、质量和方法进行了亚组分析，但并未发现异质性的原因。Egger 和 Begg 检验未显示出显著的发表偏倚。
体积乳腺密度与乳腺癌风险密切相关，强调了在筛查计划中准确评估乳腺密度的重要性。像 Volpara™ 和 Quantra™ 这样的自动体积测量工具提供了可靠的评估，可能会提高乳腺癌风险预测和管理的准确性。
实施完全自动化的乳腺密度评估工具可以提高临床实践的一致性，最大限度地减少观察者的变异性，提高筛查的准确性。这些工具应进一步针对标准化标准进行验证，以确保在不同的临床环境中的可靠性。
