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PLoS One. 2013 Dec 23;8(12):e83806. doi: 10.1371/journal.pone.0083806. eCollection 2013.

Growth pattern analysis of murine lung neoplasms by advanced semi-automated quantification of micro-CT images.

Author information

1
Department of Biomedical Sciences, Cornell University, Ithaca, New York, United States of America.
2
School of Electrical and Computer Engineering, Cornell University, Ithaca, New York, United States of America.
3
Institute for Biotechnology and Life Science Technologies, Cornell University, Ithaca, New York, United States of America.

Abstract

Computed tomography (CT) is a non-invasive imaging modality used to monitor human lung cancers. Typically, tumor volumes are calculated using manual or semi-automated methods that require substantial user input, and an exponential growth model is used to predict tumor growth. However, these measurement methodologies are time-consuming and can lack consistency. In addition, the availability of datasets with sequential images of the same tumor that are needed to characterize in vivo growth patterns for human lung cancers is limited due to treatment interventions and radiation exposure associated with multiple scans. In this paper, we performed micro-CT imaging of mouse lung cancers induced by overexpression of ribonucleotide reductase, a key enzyme in nucleotide biosynthesis, and developed an advanced semi-automated algorithm for efficient and accurate tumor volume measurement. Tumor volumes determined by the algorithm were first validated by comparison with results from manual methods for volume determination as well as direct physical measurements. A longitudinal study was then performed to investigate in vivo murine lung tumor growth patterns. Individual mice were imaged at least three times, with at least three weeks between scans. The tumors analyzed exhibited an exponential growth pattern, with an average doubling time of 57.08 days. The accuracy of the algorithm in the longitudinal study was also confirmed by comparing its output with manual measurements. These results suggest an exponential growth model for lung neoplasms and establish a new advanced semi-automated algorithm to measure lung tumor volume in mice that can aid efforts to improve lung cancer diagnosis and the evaluation of therapeutic responses.

PMID:
24376755
PMCID:
PMC3871568
DOI:
10.1371/journal.pone.0083806
[Indexed for MEDLINE]
Free PMC Article
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