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Quantitative monitoring of mouse lung tumors by magnetic resonance imaging

Nature Protocols volume 7, pages 128–142 (2012)Cite this article

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Abstract

Primary lung cancer remains the leading cause of cancer-related death in the Western world, and the lung is a common site for recurrence of extrathoracic malignancies. Small-animal (rodent) models of cancer can have a very valuable role in the development of improved therapeutic strategies. However, detection of mouse pulmonary tumors and their subsequent response to therapy in situ is challenging. We have recently described MRI as a reliable, reproducible and nondestructive modality for the detection and serial monitoring of pulmonary tumors. By combining respiratory-gated data acquisition methods with manual and automated segmentation algorithms described by our laboratory, pulmonary tumor burden can be quantitatively measured in approximately 1 h (data acquisition plus analysis) per mouse. Quantitative, analytical methods are described for measuring tumor burden in both primary (discrete tumors) and metastatic (diffuse tumors) disease. Thus, small-animal MRI represents a novel and unique research tool for preclinical investigation of therapeutic strategies for treatment of pulmonary malignancies, and it may be valuable in evaluating new compounds targeting lung cancer in vivo.

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Figure 1: Sample calibration curve for the estimation of absolute lung tumor burden.
Figure 2: Graphic illustrating three different approaches to measuring diffuse tumor burden.
Figure 3
Figure 4: Manual segmentation of discrete tumors.
Figure 5: Manual segmentation of diffuse metastatic tumors.
Figure 6: Automated segmentation of diffuse metastatic tumors.

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Acknowledgements

This work was supported by a Mr. and Mrs. Spencer T. Olin Fellowship for Women in Graduate Study; National Science Foundation Grant CCF-0963742; the NIH/National Cancer Institute Small Animal Imaging Resource Program (U24 CA83060); the Alvin J. Siteman Cancer Center at Washington University in St. Louis, an NCI Comprehensive Cancer Center (P30 CA91842); American Cancer Society Internal Research Grant from the Siteman Cancer Center, NIH/National Cancer Institute Grant (KO8 CA131097); an American Thoracic Society/Lungevity Foundation Research Grant; the Barnes-Jewish Hospital Foundation, and the generous support of Sheldon and Charlotte Rudnick.

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Author notes

  1. Alexander Sasha Krupnick and Vanessa K Tidwell: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, USA

    Alexander Sasha Krupnick, Vamsi V Alli, Andrew E Gelman & Daniel Kreisel

  2. Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, USA

    Vanessa K Tidwell & Arye Nehorai

  3. Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA

    John A Engelbach & Joel R Garbow

  4. Medical College of Wisconsin Cancer Center, Milwaukee, Wisconsin, USA

    Ming You & Haris G Vikis

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Contributions

A.S.K., V.K.T. and J.R.G. designed the imaging experiments, performed the data analysis and wrote the manuscript; J.A.E. collected all the MR images; A.E.G. and D.K. helped with experimental design; A.N. helped with data analysis; V.V.A. provided technical support; M.Y. and H.G.V. provided lung tumor-bearing animals and were instrumental in initial efforts to use MRI for monitoring primary mouse lung tumors.

Corresponding author

Correspondence to Joel R Garbow.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Large equipment necessary for acquisition of MR Images. (PDF 737 kb)

Supplementary Fig. 2

Small-animal equipment necessary for acquisition of MR images. (PDF 446 kb)

Supplementary Video 1

Manual segmentation of mouse lungs using ImageJ. (AVI 23719 kb)

Supplementary Video 2

Running the Matlab Lung_Segmenation_Main.m program to automatically segment mouse lungs and compute average lung-image intensity, as required to measure tumor burden within the lungs. (AVI 14036 kb)

Supplementary Data

Matlab Code. (ZIP 20 kb)

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Krupnick, A., Tidwell, V., Engelbach, J. et al. Quantitative monitoring of mouse lung tumors by magnetic resonance imaging. Nat Protoc 7, 128–142 (2012). https://doi.org/10.1038/nprot.2011.424

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