Volumetric CT-based segmentation of NSCLC using 3D-Slicer

Emmanuel Rios Velazquez*, Chintan Parmar, Mohammed Jermoumi, Raymond H. Mak, Angela van Baardwijk, Fiona M. Fennessy, John H. Lewis, Dirk De Ruysscher, Ron Kikinis, Philippe Lambin, Hugo J. W. L. Aerts

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

121 Citations (Web of Science)

Abstract

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.
Original languageEnglish
Article number3529
JournalScientific Reports
Volume3
DOIs
Publication statusPublished - 18 Dec 2013

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