Lung cancer is the most common cancer worldwide. Early detection might reduce morbidity. In this study we evaluate a microCT imaging algorithm to assess in-vivo tumour load and quantification of tumour growth in a transgenic disease model of lung adenocarcinomas. MicroCT was carried out with n=10 SPC-raf transgenic mice without gating in spontaneously breathing and isoflurane anaesthetised animals. Segmentation of the air-filled spaces was obtained using a region growing algorithm by 3 independent observers. Inter- and intra-observer variability of the algorithm was determined and compared against an alternative region growing algorithm. Due to the multiple very small tumor nodules that occur and the low signal-to-noise ratio direct volumetric measurement of solitary tumor nodules is not feasible. However, tumor growth can be assessed by measuring the decrease in the segmented volume of the aerated lung areas. The presented algorithm can thus be used to evaluate therapeutic efficacies of novel treatment strategies. The imaging algorithm allows in vivo quantification of lung tumor load and tumor growth in transgenic mice with an acceptable intra- and interobserver variability.