EFFECTIVENESS AND COST-EFFECTIVENESS OF DOUBLE READING IN DIGITAL MAMMOGRAPHY SCREENING: A SYSTEMATIC REVIEW AND META-ANALYSIS

Jonathan Billy Christian Tjiayadi, Deborah Josephine Theresia
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Abstract

Background: Female breast cancer has become the global leading cause of cancer-related mortality. Although digital mammography has been proposed as an effective and cost-efficient screening method, its real performance and cost-benefit value has been debated by several studies especially concerning the available reading methods. Double reading of digital mammography has been said to increase reading sensitivity but often found some challenges in terms of cost and false positive rate. This systematic review and meta-analysis aim to evaluate the effectiveness and cost-effectiveness of double reading for digital mammography screening.  Methods: This review included comparative studies and cost-effectiveness studies from databases such as Pubmed and Cochrane up to April 2023. We excluded non-English studies, cost-effectiveness studies with lacking adequate statistics, single-armed trials, study protocols, earlier meta-analyses, review articles, and studies that merely evaluated double reading of two different methodologies. Study quality was assessed using the QUADAS-2 tool and CHEERS 2022 checklist. Meta-analysis was conducted to evaluate cancer detection and false positive rate of double reading.  Ten studies were included in this review, three of which were obtained from a reference article. Mammograms in this review were obtained from a total of 260,501 women. Double reading had a slightly but significant chance of finding a breast cancer (OR = 1.137; p-value = 0.004). False-positive rate in double reading was also prominent (ER = 0.041; p value = 0.000). Single reading with CAD was still proven to be a more cost-effective method.  Discussion: Studies in this review was generally had low risk of bias and applicability concern. High cost of double reading may be attributed to the high number of false positive result. Integration of CAD with AI or deep learning may enhance the performance of digital mammography single reading.  Conclusion:  with consensus and arbitration, double reading strategy present itself as a screening method for breast cancer, however single reading with CAD has proven more superior as a more-cost effective method.
数字乳房x线摄影筛查中双重阅读的有效性和成本效益:系统回顾和荟萃分析
背景:女性乳腺癌已成为全球癌症相关死亡的主要原因。尽管数字乳房x线摄影已被认为是一种有效且成本效益高的筛查方法,但其实际性能和成本效益价值在一些研究中一直存在争议,特别是关于可用的阅读方法。数字乳房x线摄影的双重读数据说可以提高读取灵敏度,但经常发现在成本和假阳性率方面存在一些挑战。本系统综述和荟萃分析旨在评估数字乳房x线摄影筛查的双重读数的有效性和成本效益。方法:本综述纳入了截至2023年4月Pubmed和Cochrane等数据库的比较研究和成本-效果研究。我们排除了非英语研究、缺乏足够统计数据的成本效益研究、单臂试验、研究方案、早期荟萃分析、综述文章和仅评估两种不同方法的双重阅读的研究。使用QUADAS-2工具和CHEERS 2022检查表评估研究质量。采用meta分析评估双重读数的癌症检出率和假阳性率。本综述纳入了10项研究,其中3项来自参考文献。本综述中的乳房x线照片来自260,501名女性。双读有轻微但显著的发现乳腺癌的机会(OR = 1.137;p值= 0.004)。双读假阳性率也显著(ER = 0.041;P值= 0.000)。单次读取与CAD仍然被证明是一个更经济有效的方法。讨论:本综述中的研究普遍存在低偏倚风险和适用性问题。重读成本高可能是由于假阳性结果较多。CAD与人工智能或深度学习的集成可以提高数字乳房x线摄影单次读取的性能。结论:经共识和仲裁,双读策略作为乳腺癌筛查的一种方法,但CAD单读被证明是一种更具成本效益的方法。
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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