Velazquez, Emmanuel RiosParmar, ChintanJermoumi, MohammedMak, Raymondvan Baardwijk, AngelaFennessy, FionaLewis, John H.De Ruysscher, DirkKikinis, RonLambin, PhilippeAerts, Hugo2014-03-112013Velazquez, E. R., C. Parmar, M. Jermoumi, R. H. Mak, A. van Baardwijk, F. M. Fennessy, J. H. Lewis, et al. 2013. “Volumetric CT-based segmentation of NSCLC using 3D-Slicer.” Scientific Reports 3 (1): 3529. doi:10.1038/srep03529. http://dx.doi.org/10.1038/srep03529.2045-2322http://nrs.harvard.edu/urn-3:HUL.InstRepos:11879380Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the “gold standard”. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81–0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck.en-USJournal Article2014-03-1110.1038/srep03529