Journal
SCIENTIFIC REPORTS
Volume 3, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/srep03529
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Funding
- National Institute of Health (NIH-USA) [U01 CA 143062-01]
- CTMM framework (AIRFORCE project) [030-103]
- EU
- euroCAT (IVA Interreg)
- Kankeronderzoekfonds Limburg from the Health Foundation Limburg
- Dutch Cancer Society [KWF UM 2011-5020, KWF UM 2009-4454]
- QuIC-ConCePT project [115151]
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Accurate 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.
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