Kisiel John B, Ebbert Jon O, Taylor William R, Marinac Catherine R, Choudhry Omair A, Rego Seema P, Beer Tomasz M, Beidelschies Michelle A
Mayo Clinic, Rochester, MN 55905, USA.
Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Life (Basel). 2024 Jul 24;14(8):925. doi: 10.3390/life14080925.
Guideline-recommended screening programs exist for only a few cancer types. Although all these programs are understood to lead to reductions in cancer-related mortality, standard-of-care screening tests vary in accuracy, adherence and effectiveness. Recent advances in high-throughput technologies and machine learning have facilitated the development of blood-based multi-cancer cancer early detection (MCED) tests. MCED tests are positioned to be complementary to standard-of-care screening and they may broaden screening availability, especially for individuals who are not adherent with current screening programs and for individuals who may harbor cancers with no available screening options. In this article, we outline some key features that should be considered for study design and MCED test development, provide an example of the developmental pathway undertaken for an emerging multi-biomarker class MCED test and propose a clinical algorithm for an imaging-based diagnostic resolution strategy following MCED testing.
仅针对少数几种癌症类型存在指南推荐的筛查项目。尽管所有这些项目都被认为能降低癌症相关死亡率，但标准治疗筛查测试在准确性、依从性和有效性方面存在差异。高通量技术和机器学习的最新进展推动了基于血液的多癌早期检测（MCED）测试的发展。MCED测试旨在补充标准治疗筛查，它们可能会扩大筛查的可及性，特别是对于不遵守当前筛查项目的个体以及可能患有尚无可用筛查方法的癌症的个体。在本文中，我们概述了研究设计和MCED测试开发应考虑的一些关键特征，提供了一种新兴的多生物标志物类MCED测试所采用的开发途径示例，并提出了一种基于成像的MCED测试后诊断分辨率策略的临床算法。
