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
{"title":"Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon.","authors":"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","doi":"10.1002/1878-0261.13783","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18764,"journal":{"name":"Molecular Oncology","volume":" ","pages":""},"PeriodicalIF":6.6000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/1878-0261.13783","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
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.
Molecular OncologyBiochemistry, 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.