<scp>MetFinder</scp>: A Tool for Automated Quantitation of Metastatic Burden in Histological Sections From Preclinical Models
As efforts to study the mechanisms of melanoma metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate the effects on tumor burden resulting from specific interventions. We show that automated quantification of tumor content from whole slide images is a compelling solution to assess in vivo experiments. In order to increase the outflow of data collection from preclinical studies, we assembled a large dataset with annotations and trained a deep neural network for the quantitative analysis of melanoma tumor content on histopathological sections of murine models. After assessing its performance in segmenting these images, the tool obtained consistent results with an orthogonal method (bioluminescence) of measuring metastasis in an experimental setting. This AI-based algorithm, made freely available to academic laboratories through a web-interface called MetFinder, promises to become an asset for melanoma researchers and pathologists interested in accurate, quantitative assessment of metastasis burden.
Authors
- Arman Alberto Sorin Shadaloey
- Eva Hernando (ORCID: https://orcid.org/0000-0003-1023-0312)
- Daniel Mário de Lima (ORCID: https://orcid.org/0000-0002-7818-6103)
- Kristyn Galbraith (ORCID: https://orcid.org/0000-0002-5660-866X)
- Alcida Karz (ORCID: https://orcid.org/0000-0001-5839-3423)
- Grace Levinson
- Cynthia A. Loomis (ORCID: https://orcid.org/0000-0001-8668-1270)
- Rana S. Moubarak (ORCID: https://orcid.org/0000-0003-2096-1258)
- Aristotelis Tsirigos (ORCID: https://orcid.org/0000-0002-7512-8477)
- Nicolas Coudray (ORCID: https://orcid.org/0000-0002-6050-2219)
- Lucia B. Jilaveanu (ORCID: https://orcid.org/0000-0001-8900-5083)
- Gillian Baptiste (ORCID: https://orcid.org/0000-0001-8783-8372)
- Valeria Mezzano (ORCID: https://orcid.org/0000-0003-4675-9146)
- Erol C. Bayraktar (ORCID: https://orcid.org/0000-0002-2950-0215)
- George Jour (ORCID: https://orcid.org/0000-0001-8916-8966)
- Maya Navarro (ORCID: https://orcid.org/0000-0001-7850-3677)
- Mark Alu
- Nicole M. Eskow (ORCID: https://orcid.org/0000-0003-1048-4769)
- Andrey Rubanov (ORCID: https://orcid.org/0000-0002-5007-0360)
- Adam Rubens
Institutions
- Yale University (US)
- Eastern Cooperative Oncology Group (US)
- NYU Langone Health (US)
- New York University (US)
Publication Details
- Journal
- Pigment Cell & Melanoma Research
- Published
- 2024-09-10
- DOI
- https://doi.org/10.1111/pcmr.13195
- Citations
- 1
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.66
Funders
- U.S. Department of Defense
- Melanoma Research Alliance
- National Institutes of Health
- NYU Grossman School of Medicine
- National Cancer Institute
- National Center for Advancing Translational Sciences
- Congressionally Directed Medical Research Programs