Related Experiment Video

Updated: May 5, 2026

Evaluation of Lung Metastasis in Mouse Mammary Tumor Models by Quantitative Real-time PCR
08:56

Evaluation of Lung Metastasis in Mouse Mammary Tumor Models by Quantitative Real-time PCR

Published on: January 29, 2016

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

Alcida Karz1,2, Nicolas Coudray3,4, Erol Bayraktar1,2

  • 1Department of Pathology, NYU Grossman School of Medicine, New York, New York, USA.

Pigment Cell & Melanoma Research
|September 10, 2024
PubMed

Insights

Researchers developed an AI tool, MetFinder, for accurate, high-throughput quantification of melanoma tumor burden in preclinical studies. This method analyzes whole slide images, aiding in evaluating therapeutic interventions and accelerating data collection from experiments.

Area of Science:

  • Oncology
  • Computational Pathology
  • Bioinformatics

Background:

  • Studying melanoma metastasis and developing new therapies requires efficient methods to measure tumor burden.
  • Current methods for assessing tumor burden in preclinical studies can be time-consuming and lack high-throughput capabilities.

Purpose of the Study:

  • To develop and validate an automated, AI-based tool for accurate quantification of melanoma tumor content in histopathological images.
  • To provide a high-throughput solution for assessing tumor burden in preclinical melanoma research.

Main Methods:

  • Assembled a large annotated dataset of histopathological sections from murine melanoma models.
  • Trained a deep neural network for automated segmentation and quantification of tumor content in whole slide images.
  • Validated the AI tool's performance against an orthogonal method (bioluminescence) for measuring metastasis.

Main Results:

  • The AI-based tool accurately quantifies melanoma tumor content from whole slide images.
  • The automated quantification showed consistent results compared to bioluminescence measurements.
  • The developed algorithm, MetFinder, is made freely available via a web interface.

Conclusions:

  • Automated quantification of tumor content using AI is a powerful solution for assessing in vivo melanoma metastasis.
  • MetFinder offers an accurate, high-throughput, and accessible tool for melanoma researchers and pathologists.
  • This AI-driven approach can significantly enhance data collection and analysis in preclinical cancer studies.
Keywords:
deep learninghistopathologymetastatic burdenmurine modelspreclinical studiesquantificationwhole slide images

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