ArticleBJR artificial intelligence2024
Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence.
Article in BJR artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Radiomics and machine learning analysis of ultrasound images in Bethesda IV thyroid nodules: a retrospective monocentric study.Updates in surgery · 2026Article
- Bridging In Vitro and Murine Breast Cancer Models: Advanced Imaging Across Multiscale Experimental Platforms.Cancers · 2026Review
- Longitudinal Evolution of Radiomic Features in Radiation-Induced Necrosis During Follow-Up of Brain Metastases: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- Multifunctional Nano-Contrast Agent Carriers: From Traditional Platforms to Next-Generation Theranostic Applications in Molecular Imaging.Biomedicines · 2026Review
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Prospective validation of an AI software for detecting clinically significant prostate cancer on biparametric MRI.Insights into imaging · 2026Article
- Predictive value ofFrontiers in oncology · 2026Article
- Comparing Handcrafted Radiomics Versus Latent Deep Learning Features of Admission Head CT for Hemorrhagic Stroke Outcome Prediction.Biotech (Basel (Switzerland)) · 2025Article
- Recent Advances in Magnetic Resonance Imaging for the Diagnosis of Liver Cancer: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of radiomics has revolutionized medical image analysis, affording the extraction of high dimensional quantitative data for the detailed examination of normal and abnormal tissues. Artificial intelligence (AI) can be used for the enhancement of a series of steps in the radiomics pipeline, from image acquisition and preprocessing, to segmentation, feature extraction, feature selection, and model development. The aim of this review is to present the most used AI methods for radiomics analysis, explaining the advantages and limitations of the methods. Some of the most prominent AI architectures mentioned in this review include Boruta, random forests, gradient boosting, generative adversarial networks, convolutional neural networks, and transformers. Employing these models in the process of radiomics analysis can significantly enhance the quality and effectiveness of the analysis, while addressing several limitations that can reduce the quality of predictions. Addressing these limitations can enable high quality clinical decisions and wider clinical adoption. Importantly, this review will aim to highlight how AI can assist radiomics in overcoming major bottlenecks in clinical implementation, ultimately improving the translation potential of the method.
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.