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Related Experiment Video

Updated: Nov 1, 2025

Using Micro-computed Tomography for the Assessment of Tumor Development and Follow-up of Response to Treatment in a Mouse Model of Lung Cancer
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Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.

Mary Katherine Montgomery1, John David1, Haikuo Zhang2

  • 1Comparative Medicine, Pfizer Inc., La Jolla, CA, United States of America.

Plos One
|June 17, 2021
PubMed
Summary

A new automated tool, the Mouse Lung Automated Segmentation Tool (MLAST), significantly speeds up the analysis of lung cancer in mice. This innovation aids in developing more effective lung cancer therapies.

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Area of Science:

  • Preclinical Cancer Research
  • Medical Imaging Analysis
  • Computational Biology

Background:

  • Lung cancer survival rates have stagnated since the 1970s, necessitating improved research tools.
  • Genetically engineered mouse models offer high translational relevance for studying human lung cancer mutations.
  • Traditional methods for quantifying lung tumors in mice are insufficient for longitudinal studies and therapy response assessment.

Purpose of the Study:

  • To develop a fully-automated alternative to manual segmentation for analyzing micro-computed tomography (microCT) images of mouse lung tumors.
  • To create the Mouse Lung Automated Segmentation Tool (MLAST) for precise and efficient quantification of tumor burden.
  • To validate MLAST's performance against established methods and assess its utility in preclinical drug efficacy trials.

Main Methods:

  • Development of the Mouse Lung Automated Segmentation Tool (MLAST) for automated thoracic region identification and lung field segmentation.
  • MLAST categorizes lung tissue into soft tissue, intermediate, and lung categories to quantify tumor burden.
  • Validation of MLAST against manual scoring, manual segmentation, and histology; application in a Kras/Lkb1 non-small cell lung cancer efficacy study.

Main Results:

  • MLAST accurately quantifies tumor burden by measuring decreased lung volume and increased soft/intermediate tissue.
  • Validation confirmed MLAST's precision and sensitivity compared to manual methods and histology.
  • Application in an efficacy trial demonstrated MLAST's ability to precisely quantify tumor growth inhibition following drug treatment.

Conclusions:

  • MLAST provides a fully-automated, efficient, and validated method for analyzing microCT lung cancer data in mice.
  • The tool accelerates data analysis, enabling larger study sizes and mid-study therapeutic readouts.
  • Automated image analysis tools like MLAST are crucial for high-throughput, quantitative results in preclinical imaging research.