ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2015
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05331040 (The New Challenge of Decoding Rectal Cancer Signatures By Non-Invasive Imaging), which is not on this map. Cited by 392 papers, 4 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The New Challenge of Decoding Rectal Cancer Signatures By Non-Invasive Imaging: A Retrospective Radiomics Study
Who cites it
392 citing papers in PubMed, 4 syntheses or guidelines pooled it.
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- The application of radiomics in laryngeal cancer.The British journal of radiology · 2021Pooled it
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- Noninvasive Method for Predicting the Expression of Ki67 and Prognosis in Non-Small-Cell Lung Cancer Patients: Radiomics.Journal of healthcare engineering · 2022Trial
- Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images.BMC oral health · 2021Trial
- Identification of high-risk carotid plaque with MRI-based radiomics and machine learning.European radiology · 2021Trial
- Radiomics-Based Prediction of Treatment Response in Non-Small Cell Lung Cancer Using Pre-Treatment CT Imaging Features.Diagnostics (Basel, Switzerland) · 2026Article
- Preoperative CT-Based Habitat Radiomics Classifiers Predict Recurrence in Non-Small Cell Lung Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Frequency Ranking of Imaging Biomarkers for Lung Cancer Risk Stratification Using a Hybrid Elastic Net Method.Cancers · 2026Article
- A Noninvasive Predictive Model of Portal Hypertension in Patients with HCC Based on Clinical Features and Intra- and Peritumoral Pre-Fusion Radiomic Features.Journal of hepatocellular carcinoma · 2026Article
- Radiomics-based high-resolution CT analysis for differentiating primary tumor sources of pulmonary metastases.Frontiers in oncology · 2026Article
- Multimodal Radiomics Model Combining HR-VWI and Clinical Features for Identifying Symptomatic Basilar Atherosclerotic Plaques.BioMed research international · 2026Article
- Prediction of brain metastasis in patients with epidermal growth factor receptor-positive lung adenocarcinoma based on lung computed tomography-derived radiomics features.BMC medical imaging · 2025Article
- CT based quantification of intratumoral and peritumoral heterogeneity for diagnosing lymphovascular invasion for early stage non-small cell lung cancer.BMC medical imaging · 2025Article
- Artificial Intelligence as a Diagnostic Tool in Preoperative Surgical Planning for Early Non-Small Cell Lung Cancer: A Single-Center Experience.Journal of clinical medicine · 2025Article
- The pitfalls of fixed-ratio data splitting in radiomics model performance evaluation.Abdominal radiology (New York) · 2025Review
- Multi-institutional development and validation of habitat imaging for predicting outcomes of first-line immunotherapy in advanced non-small cell lung cancer.Translational lung cancer research · 2025Article
- Review
332 more citing papers are in PubMed but not listed here.
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10 authors.
Funding
Abstract
background and purposeRadiomics 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
methodsWe 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).
resultsThirty-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)).
conclusionsAlthough 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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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.