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Related Experiment Video

Updated: Jun 6, 2025

Obtaining Cancer Stem Cell Spheres from Gynecological and Breast Cancer Tumors
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AQSA-Algorithm for Automatic Quantification of Spheres Derived from Cancer Cells in Microfluidic Devices.

Ana Belén Peñaherrera-Pazmiño1,2, Ramiro Fernando Isa-Jara2,3, Elsa Hincapié-Arias4,5

  • 1Centro de Investigación Biomédica (CENBIO), Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito 170527, Ecuador.

Journal of Imaging
|November 26, 2024
PubMed
Summary
This summary is machine-generated.

A new algorithm, ASQA, automates cancer stem cell (CSC) sphere counting and measurement, improving chemoresistance prediction. This tool offers accurate, efficient, and accessible quantification for cancer research, potentially reducing treatment costs and toxicity.

Keywords:
CSCsalgorithmartificial intelligencecancermicrofluidicsprediction

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Area of Science:

  • Oncology
  • Bioinformatics
  • Cancer Research

Background:

  • Cancer stem cells (CSCs) are crucial in chemoresistance and recurrence.
  • Sphere formation assays enrich CSCs but manual counting is laborious and subjective.
  • Automated quantification is needed for reliable prediction chemotherapy assays.

Purpose of the Study:

  • To develop and validate an automated computational program, the Automatic Quantification of Spheres Algorithm (ASQA), for detecting, counting, and measuring CSC spheres.
  • To compare ASQA's performance against manual counts and existing software (SpheroidJ).
  • To assess ASQA's utility in observing treatment response and its applicability in resource-limited settings.

Main Methods:

  • Development of the Automatic Quantification of Spheres Algorithm (ASQA).
  • Comparison of ASQA counts with manual counts (p = 0.167).
  • Comparison of ASQA area measurements with SpheroidJ for single spheres (p = 0.173).
  • Evaluation of ASQA performance with uniform and nonuniform backgrounds.
  • Testing ASQA on LN229 cell line and primary culture CSCs.

Main Results:

  • ASQA showed no statistically significant difference compared to manual counts.
  • ASQA demonstrated high accuracy in area measurements, comparable to SpheroidJ.
  • The algorithm efficiently detects and quantifies multiple spheres, enabling treatment response observation.
  • ASQA functions effectively with uniform backgrounds; non-uniformity may introduce artifacts.
  • The algorithm processes images rapidly (0.3s/image) with low computational cost.

Conclusions:

  • ASQA provides an automated, accurate, and efficient method for CSC sphere quantification.
  • The algorithm is adaptable for both cell lines and primary cultures.
  • ASQA's accessibility and speed can benefit cancer research globally, aiding in developing better chemotherapy strategies.
  • Automated CSC detection can help avoid ineffective treatments and reduce patient toxicity.