Cell Invasion Analysis of Tumor Spheroids Using 2D Image Data

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.