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

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

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Automation
  • Cell Proliferation
  • Image Processing, Computer-Assisted*
  • Lung Neoplasms / diagnostic imaging*
  • Lung Neoplasms / pathology*
  • Mice
  • Tumor Burden
  • X-Ray Microtomography*

Grant support

This research was funded by NIH R01 grants CA108773 and CA163255 (RSW) and an Innovation Grant from the Cornell University Institute for Biotechnology & Life Science Technologies (RSW and APR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.