Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon.

IF 6.6 2区 医学 Q1 Biochemistry, Genetics and Molecular Biology
Molecular Oncology Pub Date : 2025-06-01 Epub Date: 2025-02-10 DOI:10.1002/1878-0261.13783
Sandhya Prabhakaran, Clarence Yapp, Gregory J Baker, Johanna Beyer, Young Hwan Chang, Allison L Creason, Robert Krueger, Jeremy Muhlich, Nathan Heath Patterson, Kevin Sidak, Damir Sudar, Adam J Taylor, Luke Ternes, Jakob Troidl, Xie Yubin, Artem Sokolov, Darren R Tyson
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引用次数: 0

Abstract

The National Cancer Institute (NCI) supports numerous research consortia that rely on imaging technologies to study cancerous tissues. To foster collaboration and innovation in this field, the Image Analysis Working Group (IAWG) was created in 2019. As multiplexed imaging techniques grow in scale and complexity, more advanced computational methods are required beyond traditional approaches like segmentation and pixel intensity quantification. In 2022, the IAWG held a virtual hackathon focused on addressing challenges in analyzing complex, high-dimensional datasets from fixed cancer tissues. The hackathon addressed key challenges in three areas: (1) cell type classification and assessment, (2) spatial data visualization and translation, and (3) scaling image analysis for large, multi-terabyte datasets. Participants explored the limitations of current automated analysis tools, developed potential solutions, and made significant progress during the hackathon. Here we provide a summary of the efforts and resultant resources and highlight remaining challenges facing the research community as emerging technologies are integrated into diverse imaging modalities and data analysis platforms.

解决癌症组织数字图像分析的持续挑战:来自黑客马拉松的资源。
美国国家癌症研究所(NCI)支持许多依靠成像技术研究癌组织的研究联盟。为了促进这一领域的合作和创新,2019年成立了图像分析工作组(IAWG)。随着多路复用成像技术在规模和复杂性方面的增长,除了分割和像素强度量化等传统方法之外,还需要更先进的计算方法。2022年,IAWG举办了一场虚拟黑客马拉松,专注于解决分析来自固定癌症组织的复杂高维数据集的挑战。黑客马拉松解决了三个领域的关键挑战:(1)细胞类型分类和评估,(2)空间数据可视化和转换,以及(3)大型,多tb数据集的缩放图像分析。参与者探索了当前自动化分析工具的局限性,开发了潜在的解决方案,并在黑客马拉松期间取得了重大进展。在这里,我们总结了这些努力和由此产生的资源,并强调了随着新兴技术被整合到各种成像模式和数据分析平台中,研究界面临的仍然存在的挑战。
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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来源期刊
Molecular Oncology
Molecular Oncology Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
11.80
自引率
1.50%
发文量
203
审稿时长
10 weeks
期刊介绍: Molecular Oncology highlights new discoveries, approaches, and technical developments, in basic, clinical and discovery-driven translational cancer research. It publishes research articles, reviews (by invitation only), and timely science policy articles. The journal is now fully Open Access with all articles published over the past 10 years freely available.
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