MetFinder: a neural network-based tool for automated quantitation of metastatic burden in histological sections from animal models
Year: 2023
Type: preprint
Abstract: Diagnosis of most diseases relies on expert histopathological evaluation of tissue sections by an experienced pathologist. By using standardized staining techniques and an expanding repertoire of markers, a trained eye is able to recognize disease-specific patterns with high accuracy and determine a diagnosis. As efforts to study mechanisms of metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate effects on tumor burden resulting from specific interventions. However, current methods of quantifying tumor burden are low in either resolution or throughput. Artificial neural networks, which can perform in-depth image analyses of tissue sections, provide an opportunity for automated recognition of consistent histopathological patterns. In order to increase the outflow of data collection from preclinical studies, we trained a deep neural network for quantitative analysis of melanoma tumor content on histopathological sections of murine models. This AI-based algorithm, made freely available to academic labs through a web-interface called MetFinder, promises to become an asset for researchers and pathologists interested in accurate, quantitative assessment of metastasis burden.
Authors: Alcida Karz, Nicolas Coudray, Erol Bayraktar, Kristyn Galbraith, George Jour, Arman Alberto Sorin Shadaloey, Nicole Eskow, Andrey Rubanov, Maya Navarro, Rana Moubarak, Gillian Baptiste, Grace Levinson, Valeria Mezzano, Mark Alu, Cynthia Loomis, Daniel Lima, Adam Rubens, Lucia Jilaveanu, Aristotelis Tsirigos, Eva Hernando
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: NYU Grossman School of Medicine
OpenAlex ID: W4386532006