Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma
Open accessAbstract
Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC≥0.897; dynamic range, DR≥0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.
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Grove et al found that measures such as spiculation or entropy gradients are strong prognostic indicators in patients with early-stage lung cancer (P < .01).
“Grove et al found these measures to be strong prognostic indicators in patients with early-stage lung cancer (P < .01) (56).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [56]
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- Authors
- Grove, O., Berglund, A. E., Schabath, M. B., Aerts, H. J. W. L., Dekker, A., Wang, H., Velazquez, E. R., Lambin, P., Gu, Y., Balagurunathan, Y., Eikman, E., Gatenby, R. A., Eschrich, S., & Gillies, R. J.
- Affiliations
- Moffitt Cancer Center (US)
- Brigham and Women's Hospital (US)
- Maastricht University (NL)
- Tianjin Medical University Cancer Institute and Hospital (CN)
- Corresponding author
- Gillies, Robert J.
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-03-04
- Volume
- 10
- Issue
- 3
- Pages
- e0118261
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4349806
- OpenAlex
- W2067959276
- Cited by
- 263
- References
- 30
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 16 · 2023: 19 · 2022: 18 · 2021: 32 · 2020: 38 · 2019: 32 · 2018: 34 · 2017: 27 · 2016: 22 · 2015: 4
- FWCI
- 23.50
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-03-08)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (NIH/NCI P50CA119997)
- National Cancer Institute (U01CA143062, P50CA119997, U01CA196405, P30CA076292, R25CA174664)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- lung adenocarcinoma, intratumoral heterogeneity, CT imaging features, prognostic factors, overall survival, tumor segmentation, reproducibility
- MeSH terms
- Adenocarcinoma of Lung, Adenocarcinoma, Aged, Female, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Male, Middle Aged, Prognosis, Retrospective Studies, Tomography, X-Ray Computed, Proportional Hazards Models, Entropy
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