Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon Journal Article
| Authors: | Prabhakaran, S.; Yapp, C.; Baker, G. J.; Beyer, J.; Chang, Y. H.; Creason, A. L.; Krueger, R.; Muhlich, J.; Patterson, N. H.; Sidak, K.; Sudar, D.; Taylor, A. J.; Ternes, L.; Troidl, J.; Yubin, X.; Sokolov, A.; Tyson, D. R. |
| Article Title: | Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon |
| 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. © 2025 The Author(s). Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies. |
| Keywords: | histopathology; neoplasm; neoplasms; colorectal cancer; quality control; image analysis; immunofluorescence; pathology; diagnostic imaging; germinal center; fluorescence in situ hybridization; transforming growth factor; imaging; fluorescence microscopy; cancer tissue; image processing, computer-assisted; image processing; tissue fixation; artifacts; national health organization; image registration; receiver operating characteristic; image segmentation; digital pathology; digital imaging; antibody labeling; data visualization; feature extraction; procedures; refraction index; cancer; humans; human; article; cells by body anatomy; deep learning; malignant neoplasm; generative adversarial network; image artifact; multilayer perceptron; clustering algorithm; neoplastic cell transformation; artifact removal; computational scalability; domain representation; feature learning (machine learning) |
| Journal Title: | Molecular Oncology |
| Volume: | 19 |
| Issue: | 6 |
| ISSN: | 1878-0261 |
| Publisher: | FEBS Press |
| Date Published: | 2025-01-01 |
| Start Page: | 1565 |
| End Page: | 1581 |
| Language: | English |
| DOI: | 10.1002/1878-0261.13783 |
| PUBMED: | 39927650 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Article -- Source: Scopus |
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