A semiautomatic CT-based ensemble segmentation of lung tumors: Comparison with oncologists’ delineations and with the surgical specimen
Open accessAbstract
Purpose To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC).Materials and methods For 20 NSCLC patients (stages Ib-IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org.Results High overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2 cm(3), mean±SD) and manual delineations (81.9±94.1 cm(3); p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96).Conclusions Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the "gold standard". This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors.
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Interoperator variability of manually contoured tumors is high.
“It is well recognized that interoperator variability of manually contoured tumors is high (38,39).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [38]
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References 50 · Cited by 120
- Authors
- Rios Velazquez, E., Aerts, H. J. W. L., Gu, Y., Goldgof, D. B., De Ruysscher, D., Dekker, A., Korn, R., Gillies, R. J., & Lambin, P.
- Affiliations
- Harvard University (US)
- Moffitt Cancer Center (US)
- University of South Florida (US)
- Maastricht University Medical Centre (NL)
- Venue
- Radiotherapy and Oncology
- Publisher
- Elsevier BV
- Published
- 2012-11
- Volume
- 105
- Issue
- 2
- Pages
- 167-173
- ISSN
- 0167-8140 (ISSN-L)
- PMC
- PMC3749821
- OpenAlex
- W1995061416
- Cited by
- 120
- References
- 50
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 3 · 2023: 13 · 2022: 13 · 2021: 11 · 2020: 14 · 2019: 15 · 2018: 13 · 2017: 12 · 2016: 8 · 2015: 5 · 2014: 3 · 2013: 2
- FWCI
- 3.29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Radiotherapy and Oncology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- European Federation of Pharmaceutical Industries and Associations
- European Commission (115151)
- KWF Kankerbestrijding
- National Institutes of Health
- National Cancer Institute (U01CA143062)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- non-small cell lung cancer, lung tumor segmentation, semi-automatic segmentation, CT image segmentation, ensemble segmentation, tumor volume, manual segmentation, CT-PET, target volume definition, radiotherapy treatment planning
- MeSH terms
- Algorithms, Humans, Lung Neoplasms, Tomography, X-Ray Computed, Positron-Emission Tomography, Multimodal Imaging
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