Longitudinal assessment of lung cancer progression in the mouse using in vivo micro-CT imaging.
OBJECTIVE
Small animal micro-CT imaging is being used increasingly in preclinical biomedical research to provide phenotypic descriptions of genomic models. Most of this imaging is coincident with animal death and is used to show the extent of disease as an end point. Longitudinal imaging overcomes the limitation of single time-point imaging because it enables tracking of the natural history of disease and provides qualitative and, where possible, quantitative assessments of the effects of an intervention. The pulmonary system is affected by many disease conditions, such as lung cancer, chronic obstructive pulmonary disease, asthma, and granulomatous disorders. Noninvasive imaging can accurately assess the lung phenotype within the living animal, evaluating not only global lung measures, but also regional pathology. However, imaging the lung in the living animal is complicated by rapid respiratory motion, which leads to image based artifacts. Furthermore, no standard mouse lung imaging protocols exist for longitudinal assessment, with each group needing to develop their own systematic approach.


METHODS
In this article, the authors present an outline for performing longitudinal breath-hold gated micro-CT imaging for the assessment of lung nodules in a mouse model of lung cancer. The authors describe modifications to the previously published intermittent isopressure breath-hold technique including a new animal preparation and anesthesia protocol, implementation of a ring artifact reduction, variable scanner geometry, and polynomial beam hardening correction. In addition, the authors describe a multitime-point data set registration and tumor labeling and tracking strategy.


RESULTS
In vivo micro-CT data sets were acquired at months 2, 3, and 4 posturethane administration in cancer mice (n = 5) and simultaneously in control mice (n = 3). 137 unique lung nodules were identified from the cancer mice while no nodules were detected in the control mice. A total of 411 nodules were segmented and labeled over the three time-points. Lung nodule metrics including RECIST, Ortho, WHO, and 3D volume were determined and extracted. A tumor incidence rate of 30.44 +/- 1.93 SEM for n = 5 was found with identification of nodules as small as 0.11 mm (RECIST) and as large as 1.66 mm (RECIST). In addition, the tumor growth and doubling rate between months 2-3 and 3-4 were calculated. Here, the growth rate was slightly higher in the second period based on the 3D volume data (0.12 +/- 0.13 to 0.13 +/- 0.17 microl) but significantly less based on the linear diameter metrics [RECIST (0.33 +/- 0.19 to 0.17 +/- 0.18 mm); Ortho (0.24 +/- 0.15 to 0.16 +/- 0.15 mm)], indicating the need to understand how each metric is obtained and how to correctly interpret change in tumor size.


CONCLUSIONS
In conclusion, micro-CT imaging provides a unique platform for in vivo longitudinal assessment of pulmonary lung cancer progression and potentially tracking of therapies at very high resolutions. The ability to evaluate the same subject over time provides for a sensitive assay that can be carried out on a smaller sample size. When integrated with image processing and analysis routines as detailed in this study, the data acquired from micro-CT imaging can now provide a very powerful assessment of pulmonary disease outcomes.
Department of Internal Medicine, University of Iowa, Iowa City, Iowa 52242, USA; School of Computer Science, Engineering and Mathematics, Flinders University, Adelaide 5042, Australia. eman-namati@uiowa.edu
Eman
E
Namati
Jacqueline
J
Thiesse
Jessica C
JC
Sieren
Alan
A
Ross
Eric A
EA
Hoffman
Geoffrey
G
McLennan
D000758:Anesthesia; D000818:Animals; D018450:Disease Progression; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D051379:Mice; D011857:Radiographic Image Interpretation, Computer-Assisted; D013997:Time Factors; D055114:X-Ray Microtomography / Q000379:methods*