ReviewNuclear medicine and molecular imaging2018
CT Radiomics in Thoracic Oncology: Technique and Clinical Applications.
Review in Nuclear medicine and molecular imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis.BMC medical imaging · 2023Pooled it
- Listening to Immunotherapy - Ultrasound and Photoacoustic Imaging Strategies to Guide and Enhance Cancer Treatments.Molecular imaging and biology · 2026Review
- A Comprehensive Review of Radiomics in Pulmonary Nodule Management: Clinical Applications and Standardization Dilemmas.Current medical imaging · 2026Review
- Quantifying Early-Stage Lung Adenocarcinoma Progression with a Radiomic Trajectory.NPJ digital medicine · 2025Article
- Machine learning models using dual-phase CT radiomics for early detection of PRISm.Scientific reports · 2025Article
- Radiomics on slice-reduced versus full-chest computed tomography for diagnosis and staging of interstitial lung disease in systemic sclerosis: A comparative analysis.European journal of radiology open · 2024Article
- Improving prediction accuracy of spread through air spaces in clinical-stage T1N0 lung adenocarcinoma using computed tomography imaging models.JTCVS open · 2024Article
- Omics-imaging signature-based nomogram to predict the progression-free survival of patients with hepatocellular carcinoma after transcatheter arterial chemoembolization.World journal of clinical cases · 2024Article
- Article
- Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses.Lung cancer (Amsterdam, Netherlands) · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Nondiagnostic, radial-probe endobronchial ultrasound-guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy.Thoracic cancer · 2023Article
- CT-based radiomics signature for differentiation between cardiac tumors and a thrombi: a retrospective, multicenter study.Scientific reports · 2022Article
- Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study.Insights into imaging · 2022Article
- CT Radiomic Features for Predicting Resectability and TNM Staging in Thymic Epithelial Tumors.The Annals of thoracic surgery · 2022Article
- Article
- Do we need to see to believe?-radiomics for lung nodule classification and lung cancer risk stratification.Journal of thoracic disease · 2020Review
- Multi-window CT based Radiomic signatures in differentiating indolent versus aggressive lung cancers in the National Lung Screening Trial: a retrospective study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2019Article
- Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer.Scientific reports · 2019Article
- The impact of radiomics in predicting oncologic behavior of thymic epithelial tumors.Mediastinum (Hong Kong, China) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine offers better treatment options and improved survival for cancer patients based on individual variability. As the success of precision medicine depends on robust biomarkers, the requirement for improved imaging biomarkers that reflect tumor biology has grown exponentially. Radiomics, the field of study in which high-throughput data are generated and large amounts of advanced quantitative features are extracted from medical images, has shown great potential as a source of quantitative biomarkers in the field of oncology. Radiomics provides quantitative information about the morphology, texture, and intratumoral heterogeneity of the tumor itself as well as features related to pulmonary function. Hence, radiomics data can be used to build descriptive and predictive clinical models that relate imaging characteristics to tumor biology phenotypes. In this review, we describe the workflow of CT radiomics, types of CT radiomics, and its clinical application in thoracic oncology.
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What OpenQuestion holds
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.