ReviewDiagnostic and interventional radiology (Ankara, Turkey)2019
Radiomics with artificial intelligence: a practical guide for beginners.
Review in Diagnostic and interventional radiology (Ankara, Turkey), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 202 papers, 14 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
202 citing papers in PubMed, 14 syntheses or guidelines pooled it.
- MRI-based radiomics for prediction of biochemical recurrence in prostate cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Radiogenomic profiling to determine BRCA alteration status-a systematic review and meta-analysis.The British journal of radiology · 2025Pooled it
- Radiomics for differentiating radiation-induced brain injury from recurrence in gliomas: systematic review, meta-analysis, and methodological quality evaluation using METRICS and RQS.European radiology · 2025Pooled it
- Radiomics-Based Diagnosis in Dentomaxillofacial Radiology: A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- Evaluating the impact of the Radiomics Quality Score: a systematic review and meta-analysis.European radiology · 2025Pooled it
- AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis.Journal of cancer research and clinical oncology · 2024Pooled it
- Are deep models in radiomics performing better than generic models? A systematic review.European radiology experimental · 2023Pooled it
- Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer.La Radiologia medica · 2023Pooled it
- Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.Neuroradiology · 2022Pooled it
- Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2021Pooled it
- Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis.BMC cancer · 2021Pooled it
- Is radiomic MRI a feasible alternative to OncotypeDX® recurrence score testing? A systematic review and meta-analysis.BJS open · 2021Pooled it
- Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis.Contrast media & molecular imaging · 2020Pooled it
- The diagnostic value of magnetic resonance imaging-based texture analysis in differentiating enchondroma and chondrosarcoma.Skeletal radiology · 2023Trial
- Value of Rehabilitation Training for Children with Cerebral Palsy Diagnosed and Analyzed by Computed Tomography Imaging Information Features under Deep Learning.Journal of healthcare engineering · 2021Trial
- Overdiagnosis in renal cancer: the hidden consequence of modern imaging.International urology and nephrology · 2026Review
- Optimizing MRI sequence selection for glioma classification: a radiomics-based analysis of WHO CNS grade, final pathological diagnosis, and IDH status.La Radiologia medica · 2026Article
- Machine learning-based integration and comparison of ADC map radiomics with conventional imaging markers for cholesteatoma diagnosis.Neuroradiology · 2026Article
- CT imaging of thyroid cartilage invasion in glottic laryngeal cancer: correlation of traditional semiotics and texture analysis with histopathology.La Radiologia medica · 2026Article
142 more citing papers are in PubMed but not listed here.
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
No grant is acknowledged in the PubMed record.
Abstract
Radiomics is a relatively new word for the field of radiology, meaning the extraction of a high number of quantitative features from medical images. Artificial intelligence (AI) is broadly a set of advanced computational algorithms that basically learn the patterns in the data provided to make predictions on unseen data sets. Radiomics can be coupled with AI because of its better capability of handling a massive amount of data compared with the traditional statistical methods. Together, the primary purpose of these fields is to extract and analyze as much and meaningful hidden quantitative data as possible to be used in decision support. Nowadays, both radiomics and AI have been getting attention for their remarkable success in various radiological tasks, which has been met with anxiety by most of the radiologists due to the fear of replacement by intelligent machines. Considering ever-developing advances in computational power and availability of large data sets, the marriage of humans and machines in future clinical practice seems inevitable. Therefore, regardless of their feelings, the radiologists should be familiar with these concepts. Our goal in this paper was three-fold: first, to familiarize radiologists with the radiomics and AI; second, to encourage the radiologists to get involved in these ever-developing fields; and, third, to provide a set of recommendations for good practice in design and assessment of future works.
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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.