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Volumetric CT-based Segmentation of NSCLC using 3D Slicer

Institution:
1Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2Department of Radiation Oncology (MAASTRO), GROW Research Institute, Maastricht University, Maastricht, The Netherlands.
3Radiation Oncology, University Hospitals Leuven/KU Leuven, Leuven, Belgium.
4Surgical Planning Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Publisher:
Nature Publishing Group
Publication Date:
Dec-2013
Journal:
Sci Rep
Volume Number:
3
Pages:
3529
Citation:
Sci Rep. 2013 Dec 18;3:3529.
PubMed ID:
24346241
PMCID:
PMC3866632
Appears in Collections:
SPL, NAC, SLICER
Sponsors:
U01 CA143062/CA/NCI NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
Generated Citation:
Velazquez E.R., Parmar C., Jermoumi M., Mak R.H., van Baardwijk A., Fennessy F.M., Lewis J.H., De Ruysscher D., Kikinis R., Lambin P., Aerts H.J. Volumetric CT-based Segmentation of NSCLC using 3D Slicer. Sci Rep. 2013 Dec 18;3:3529. PMID: 24346241. PMCID: PMC3866632.
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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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