ReviewSeminars in ultrasound, CT, and MR2022
Radiomics: A Primer on Processing Workflow and Analysis.
Review in Seminars in ultrasound, CT, and MR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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
29 citing papers in PubMed.
- ViT-radiomics fusion for lymph node classification in ultrasound images: a multicenter study.La Radiologia medica · 2026Article
- Ultrasound Radiomics in Pediatric Imaging: Current Applications, Challenges, and Future Directions Toward Clinical Implementation.Diagnostics (Basel, Switzerland) · 2026Review
- Redefining oropharyngeal cancer in the HPV era: integrating precision medicine and immunotherapeutic frontiers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Matched MRI, Segmentations, and Histopathologic Images of Brain Metastases from Primary Lung Cancer.Scientific data · 2026Article
- Radiomics to understand pre-treatment tumor biology for resectable non-small cell lung cancer.Frontiers in oncology · 2026Review
- 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
- Artificial Intelligence Applications in Interventional Radiology.Journal of personalized medicine · 2025Review
- Radiomic imaging models for predicting breast cancer prognosis based on Interleukin-18 (IL18).BMC medical imaging · 2025Article
- Comparing Handcrafted Radiomics Versus Latent Deep Learning Features of Admission Head CT for Hemorrhagic Stroke Outcome Prediction.Biotech (Basel (Switzerland)) · 2025Article
- Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?Medicina (Kaunas, Lithuania) · 2025Review
- Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Differentiating between renal medullary and clear cell renal carcinoma with a machine learning radiomics approach.The oncologist · 2025Article
- A new paradigm in postoperative colorectal cancer surveillance: integrating advanced imaging and multi-omics.Frontiers in physiology · 2025Review
- Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT).Diagnostics (Basel, Switzerland) · 2024Article
- Preoperative prediction of CNS WHO grade and tumour aggressiveness in intracranial meningioma based on radiomics and structured semantics.Scientific reports · 2024Article
- Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.Cancers · 2024Review
- Radiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival.Diagnostics (Basel, Switzerland) · 2024Article
- Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke.Diagnostics (Basel, Switzerland) · 2024Article
- Review
- Role of PET/CT in Oropharyngeal Cancers.Cancers · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Quantitative analysis of medical images can provide objective tools for diagnosis, prognostication, and disease monitoring. Radiomics refers to automated extraction of a large number of quantitative features from medical images for characterization of underlying pathologies. In this review, we will discuss the principles of radiomics, image preprocessing, feature extraction workflow, and statistical analysis. We will also address the limitations and future directions of radiomics.
Indexed as
Identifiers
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.