ReviewJournal of personalized medicine2021
Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians.
Review in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 2 of them syntheses 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
56 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for lung cancer: a systematic review of head‑to‑head CT, FDG PET/CT, and multimodal models across screening, staging, and prognosis.BMC medical imaging · 2026Pooled it
- Precision medicine exploration of postpartum rectus abdominis muscle separation: from basic research to clinical practice.BMC surgery · 2025Pooled it
- Bridging radiomics and histopathology: cross-modality consistency of radiomic features from CT to micro-CT in NSCLC.European radiology experimental · 2026Article
- Advancing fungal sinusitis diagnosis: a radiomics and machine learning approach.BMC medical imaging · 2026Article
- CT image-based machine learning models for predicting blood eosinophil levels in acute exacerbation of chronic obstructive pulmonary disease.BMC pulmonary medicine · 2026Article
- Radiomic Assessment of Epicardial Adipose Tissue for the Prediction of Non-Calcified Coronary Atherosclerotic Plaques.Journal of cardiovascular development and disease · 2026Article
- Multi-omics to study chronic respiratory diseases and viral infections.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Drug-Induced Interstitial Lung Disease in Lung Cancer Patients.Respiration; international review of thoracic diseases · 2026Review
- A deep learning based radiomics model for differentiating intraparenchymal hematoma induced by cerebral venous thrombosis.Scientific reports · 2025Article
- Fibrotic and vascular abnormalities quantified by an AI-based model are associated with outcomes in patients with idiopathic pulmonary fibrosis.BMC pulmonary medicine · 2025Article
- Radiomics and Artificial Intelligence in Pulmonary Fibrosis.Journal of imaging informatics in medicine · 2025Review
- Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis.BMC pediatrics · 2025Article
- Automatic recognition and differentiation of pulmonary contusion and bacterial pneumonia based on deep learning and radiomics.BMC medical imaging · 2025Article
- Non-contrast computed tomography radiomics model to predict benign and malignant thyroid nodules with lobe segmentation: A dual-center study.World journal of radiology · 2025Article
- New insights into imaging of pulmonary metastases from extra-thoracic neoplasms.La Radiologia medica · 2025Review
- Intelligent Care: A Scientometric Analysis of Artificial Intelligence in Precision Medicine.Medical sciences (Basel, Switzerland) · 2025Review
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
- Predicting triage of pediatric patients in the emergency department using machine learning approach.International journal of emergency medicine · 2025Article
- CT based 3D radiomic and clinical airway examination model for evaluating mask ventilation in oral and maxillofacial surgery patients.Scientific reports · 2025Article
- Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients.Respiratory research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician's perspective.
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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.