MetFinder : A Tool for Automated Quantitation of Metastatic Burden in Histological Sections From Preclinical Models

Year: 2024

Type: article

Abstract: 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.

Source: Pigment Cell & Melanoma Research

DOI: 10.1111/pcmr.13195

Institutions: NYU Langone Health, NYU Langone’s Laura and Isaac Perlmutter Cancer Center, New York University, Yale University

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: U.S. Department of Defense, Melanoma Research Alliance, National Institutes of Health, NYU Grossman School of Medicine, National Cancer Institute, National Institute of General Medical Sciences, National Center for Advancing Translational Sciences, Congressionally Directed Medical Research Programs

OpenAlex ID: W4402394248