ArticleOncotarget2017
Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review.European radiology · 2023Pooled it
- AI-Radiomics Can Improve Inclusion Criteria and Clinical Trial Performance.Tomography (Ann Arbor, Mich.) · 2022Trial
- Interpretable machine learning classification of cedar and cypress pollen on routine Durham slides for environmental exposure assessment.Frontiers in allergy · 2026Article
- The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software.BMC cancer · 2025Article
- Gradient-Based Radiomics for Outcome Prediction and Decision-Making in PULSAR: A Preliminary Study.International journal of particle therapy · 2025Article
- Multiomics-Based Outcome Prediction in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR).Cancers · 2024Article
- Review
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Multiregional Radiomic Signatures Based on Functional Parametric Maps from DCE-MRI for Preoperative Identification of Estrogen Receptor and Progesterone Receptor Status in Breast Cancer.Diagnostics (Basel, Switzerland) · 2022Article
- Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine.Cold Spring Harbor perspectives in medicine · 2021Review
- Hypoxia-Related Radiomics and Immunotherapy Response: A Multicohort Study of Non-Small Cell Lung Cancer.JNCI cancer spectrum · 2021Article
- The Biological Meaning of Radiomic Features.Radiology · 2021Review
- Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence.Investigative radiology · 2020Review
- Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening.Scientific reports · 2020Article
- Bone Marrow and Tumor Radiomics atRadiology · 2019Article
- Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions.Medical physics · 2019Article
- Radiomics: an Introductory Guide to What It May Foretell.Current oncology reports · 2019Review
- Artificial intelligence in cancer imaging: Clinical challenges and applications.CA: a cancer journal for clinicians · 2019Review
- Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: An early report.Lung cancer (Amsterdam, Netherlands) · 2019Article
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9 authors.
Funding
Abstract
The goal of this study was to extract features from radial deviation and radial gradient maps which were derived from thoracic CT scans of patients diagnosed with lung adenocarcinoma and assess whether these features are associated with overall survival. We used two independent cohorts from different institutions for training (n= 61) and test (n= 47) and focused our analyses on features that were non-redundant and highly reproducible. To reduce the number of features and covariates into a single parsimonious model, a backward elimination approach was applied. Out of 48 features that were extracted, 31 were eliminated because they were not reproducible or were redundant. We considered 17 features for statistical analysis and identified a final model containing the two most highly informative features that were associated with lung cancer survival. One of the two features, radial deviation outside-border separation standard deviation, was replicated in a test cohort exhibiting a statistically significant association with lung cancer survival (multivariable hazard ratio = 0.40; 95% confidence interval 0.17-0.97). Additionally, we explored the biological underpinnings of these features and found radial gradient and radial deviation image features were significantly associated with semantic radiological features.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.