CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
Open accessAbstract
Background and purpose Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients.Material and methods We included two datasets: 98 patients for discovery and 84 for validation. The phenotype of the primary tumor was quantified on pre-treatment CT-scans using 635 radiomic features. Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI).Results Thirty-five radiomic features were found to be prognostic (CI>0.60, FDR<5%) for DM and twelve for survival. It is noteworthy that tumor volume was only moderately prognostic for DM (CI=0.55, p-value=2.77×10(-5)) in the discovery cohort. A radiomic-signature had strong power for predicting DM in the independent validation dataset (CI=0.61, p-value=1.79×10(-17)). Adding this radiomic-signature to a clinical model resulted in a significant improvement of predicting DM in the validation dataset (p-value=1.56×10(-11)).Conclusions Although only basic metrics are routinely quantified, this study shows that radiomic features capturing detailed information of the tumor phenotype can be used as a prognostic biomarker for clinically-relevant factors such as DM. Moreover, the radiomic-signature provided additional information to clinical data.
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In the evaluation of lung cancer and glioblastoma multiforme, radiomics has been shown to be a tool with which to assess patient prognosis.
“In the evaluation of lung cancer and in the evaluation of glioblastoma multiforme, radiomics has been shown to be a tool with which to assess patient prognosis (7).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [7]
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- Authors
- Coroller, T. P., Grossmann, P., Hou, Y., Rios Velazquez, E., Leijenaar, R. T. H., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R. H., & Aerts, H. J. W. L.
- Affiliations
- Brigham and Women's Hospital (US)
- Maastricht University (NL)
- Corresponding author
- Coroller, Thibaud P.
- Aerts, Hugo J.W.L.
- ORCID
- Venue
- Radiotherapy and Oncology
- Publisher
- Elsevier BV
- Published
- 2015-03
- Volume
- 114
- Issue
- 3
- Pages
- 345-350
- ISSN
- 0167-8140 (ISSN-L)
- PMC
- PMC4400248
- OpenAlex
- W1987054640
- Cited by
- 695
- References
- 42
- Citations by year
- 2026: 15 · 2025: 39 · 2024: 51 · 2023: 37 · 2022: 70 · 2021: 97 · 2020: 97 · 2019: 98 · 2018: 72 · 2017: 71 · 2016: 33 · 2015: 13 · 2014: 1
- FWCI
- 51.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Radiotherapy and Oncology (publishedVersion)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (acceptedVersion, other-oa)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- Health Foundation Limburg
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (10696 DuCAT)
- KWF Kankerbestrijding (UM 2009-4454, KWF MAC 2013-6089, 2011-5020, KWF UM 2009-4454, KWF UM 2011-5020, MAC 2013-6089, 2009-4454, 10696 DuCAT, 2013-6089)
- National Institutes of Health (NIH-USA U01CA190234)
- Stichting voor de Technische Wetenschappen (10696 DuCAT, 10696, n° 10696 DuCAT)
- National Cancer Institute (U01CA190234)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- lung adenocarcinoma, radiomics, distant metastasis prediction, CT imaging features, prognostic factors, tumor phenotype, survival prediction
- MeSH terms
- Adenocarcinoma of Lung, Adenocarcinoma, Adult, Aged, Aged, 80 and over, Female, Humans, Lung Neoplasms, Male, Middle Aged, Neoplasm Metastasis, Prognosis, Tomography, X-Ray Computed, Tumor Burden
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