Individual nodule tracking in micro-CT images of a longitudinal lung cancer mouse model.
We present and evaluate an automatic and quantitative method for the complex task of characterizing individual nodule volumetric progression in a longitudinal mouse model of lung cancer. Fourteen A/J mice received an intraperitoneal injection of urethane. Respiratory-gated micro-CT images of the lungs were acquired at 8, 22, and 37 weeks after injection. A radiologist identified a total of 196, 585 and 636 nodules, respectively. The three micro-CT image volumes from every animal were then registered and the nodules automatically matched with an average accuracy of 99.5%. All nodules detected at week 8 were tracked all the way to week 37, and volumetrically segmented to measure their growth and doubling rates. 92.5% of all nodules were correctly segmented, ranging from the earliest stage to advanced stage, where nodule segmentation becomes more challenging due to complex anatomy and nodule overlap. Volume segmentation was validated using a foam lung phantom with embedded polyethylene microspheres. We also correlated growth rates with nodule phenotypes based on histology, to conclude that the growth rate of malignant tumors is significantly higher than that of benign lesions. In conclusion, we present a turnkey solution that combines longitudinal imaging with nodule matching and volumetric nodule segmentation resulting in a powerful tool for preclinical research.
Cancer Imaging Laboratory, Oncology Division, Center for Applied Medical Research, University of Navarra, 55 Pio XII, 31008 Pamplona, Spain. Electronic address: rrudyanto@alumni.unav.es.
Rina D
RD
Rudyanto
Gorka
G
Bastarrika
Gabriel
G
de Biurrun
Jackeline
J
Agorreta
Luis M
LM
Montuenga
Carlos
C
Ortiz-de-Solorzano
Arrate
A
Muñoz-Barrutia
D000465:Algorithms; D000818:Animals; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D051379:Mice; D009361:Neoplasm Invasiveness; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique / Q000662:veterinary*; D014057:Tomography, X-Ray Computed / Q000662:veterinary*
Lung cancer; Micro-computed tomography; Nodule matching; Nodule segmentation; Small-animal imaging