ArticleScientific reports2017
Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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Who cites it
65 citing papers in PubMed, 118 citations in OpenAlex.
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- Multicollinearity and redundancy of the PET radiomic feature set.European radiology · 2025Article
- MRI Radiomics Analysis in the Diagnostic Differentiation of Malignant Soft Tissue Myxoid Sarcomas From Benign Soft Tissue Musculoskeletal Myxomas.Journal of magnetic resonance imaging : JMRI · 2025Article
- Automated Neuroprognostication Via Machine Learning in Neonates with Hypoxic-Ischemic Encephalopathy.Annals of neurology · 2025Article
- New frontiers in hepatocellular carcinoma: Precision imaging for microvascular invasion prediction.World journal of gastroenterology · 2025Article
- Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation.Diagnostics (Basel, Switzerland) · 2024Article
- Adenosquamous Carcinoma of the Lung: Survival, Radiologic Findings, PD-L1, and Driver Mutations.Journal of clinical medicine · 2024Article
- Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy.Translational lung cancer research · 2024Article
- A Novel Method for Evaluating Early Tumor Response Based on Daily CBCT Images for Lung SBRT.Cancers · 2023Article
- Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature.Biomedical engineering online · 2023Article
- Heterogeneity of Glycolytic Phenotype Determined byDiagnostics (Basel, Switzerland) · 2023Article
- Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment.Cancers · 2023Article
- An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening.European radiology · 2023Article
- CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients.Cancers · 2023Article
- Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study.Diagnostics (Basel, Switzerland) · 2023Article
- Comparison of Magnetic Resonance Imaging-Based Radiomics Features with Nomogram for Prediction of Prostate Cancer Invasion.International journal of general medicine · 2023Article
- A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules.Frontiers in oncology · 2023Article
- Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.Journal of clinical medicine · 2022Article
- The augmented radiologist: artificial intelligence in the practice of radiology.Pediatric radiology · 2022Review
5 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
Tumor phenotypes captured in computed tomography (CT) images can be described qualitatively and quantitatively using radiologist-defined "semantic" and computer-derived "radiomic" features, respectively. While both types of features have shown to be promising predictors of prognosis, the association between these groups of features remains unclear. We investigated the associations between semantic and radiomic features in CT images of 258 non-small cell lung adenocarcinomas. The tumor imaging phenotypes were described using 9 qualitative semantic features that were scored by radiologists, and 57 quantitative radiomic features that were automatically calculated using mathematical algorithms. Of the 9 semantic features, 3 were rated on a binary scale (cavitation, air bronchogram, and calcification) and 6 were rated on a categorical scale (texture, border definition, contour, lobulation, spiculation, and concavity). 32-41 radiomic features were associated with the binary semantic features (AUC = 0.56-0.76). The relationship between all radiomic features and the categorical semantic features ranged from weak to moderate (|Spearmen's correlation| = 0.002-0.65). There are associations between semantic and radiomic features, however the associations were not strong despite being significant. Our results indicate that radiomic features may capture distinct tumor phenotypes that fail to be perceived by naked eye that semantic features do not describe and vice versa.
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