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Related Experiment Video

Updated: Feb 24, 2026

Three-Dimensional 3D Tumor Spheroid Invasion Assay
12:19

Three-Dimensional 3D Tumor Spheroid Invasion Assay

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Cell Invasion Analysis of Tumor Spheroids Using 2D Image Data.

Matěj Přikryl1, Andrea Rousová1, Ivana Acimovic2

  • 1Department of Experimental Biology, Faculty of Science, Masaryk University, Brno 625 00, Czech Republic.

ACS Measurement Science Au
|February 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new automated method to quantify cancer cell invasion in 3D models. The algorithm accurately detects and analyzes spheroid growth and invasion, crucial for developing anti-metastasis strategies.

Keywords:
3D modelsfluorescenceimage analysisinvasionobject detectionsoftware toolspheroids

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Area of Science:

  • Oncology
  • Biotechnology
  • Image Analysis

Background:

  • Metastatic disease is a major challenge in cancer treatment.
  • Quantifying cellular invasion in 3D cancer models is vital for developing anti-metastasis strategies.
  • Existing algorithms for analyzing spheroid growth and invasion lack robustness and widespread use.

Purpose of the Study:

  • To develop and validate a novel, automated methodology for quantifying cancer cell invasion in 3D extracellular matrix models.
  • To address the limitations of current algorithms in detecting and characterizing spheroid growth and invasion.
  • To enable high-quality analysis of fluorescently labeled 3D cancer models.

Main Methods:

  • Development of two mask computation strategies for compact and boundary-losing spheroids.
  • Utilizing filtered local maxima for detecting and characterizing cells outside the spheroid mask.
  • Implementing automated evaluation with user-friendly manual adjustment for non-constant background fluorescence images.

Main Results:

  • A robust method for computing spheroid core masks adaptable to various shapes.
  • Successful detection and characterization of invading cells or cell clusters outside the spheroid mask.
  • The algorithm demonstrates effectiveness in analyzing images with challenging, non-constant backgrounds.

Conclusions:

  • The presented methodology offers a significant advancement in the automated quantification of cancer cell invasion in 3D models.
  • This tool facilitates more accurate and efficient analysis of tumor progression and invasion dynamics.
  • The developed algorithm supports the identification and evaluation of strategies to suppress metastatic disease.