ArticleExpert review of precision medicine and drug development2019
Deep learning and radiomics in precision medicine.
Article in Expert review of precision medicine and drug development, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 6 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
116 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Comparative diagnostic accuracy of radiomics, deep learning, and hybrid AI for invasiveness stratification of pulmonary ground-glass nodules: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- CT-based radiomics in predicting the efficacy of preoperative neoadjuvant chemoimmunotherapy for non-small cell lung cancer: a systematic review and meta-analysis.Frontiers in immunology · 2026Pooled it
- MRI-based radiomics for predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Application of radiomics to meningiomas: A systematic review.Neuro-oncology · 2023Pooled it
- Are deep models in radiomics performing better than generic models? A systematic review.European radiology experimental · 2023Pooled it
- Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage population.European journal of neurology · 2021Trial
- Longitudinal functional and imaging outcome measures in FKRP limb-girdle muscular dystrophy.BMC neurology · 2020Trial
- Super-resolution MRI and 2.5D deep learning for intratumoral-peritumoral radiomics in preoperative prediction of rectal cancer perineural invasion.Insights into imaging · 2026Article
- Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.Journal of clinical medicine · 2026Review
- Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma.NPJ digital medicine · 2026Article
- Evaluation methods of hepatic steatosis: From conventional techniques to emerging biomarkers.World journal of hepatology · 2026Review
- Dual-phase CT radiomics for acute kidney injury prediction after out-of-hospital cardiac arrest.Frontiers in radiology · 2026Article
- Accurate Asthma-COPD Overlap Classification via Deep Transfer Learning in Medical Image Segmentation.International journal of chronic obstructive pulmonary disease · 2026Article
- Using artificial intelligence for distinguishing benign and malignant vertebral compression fractures by computed tomography: a scoping review.International journal of burns and trauma · 2026Review
- Integrating CT-based radiomics and deep learning for invasive prediction of ground-glass nodules in lung adenocarcinoma: a multicohort study.Insights into imaging · 2025Article
- Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Assessment of a Grad-CAM interpretable deep learning model for HAPE diagnosis: performance and pitfalls in severity stratification from chest radiographs.BMC medical informatics and decision making · 2025Article
- A Priori Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Deep Features from Pre-Treatment MRI and CT.Cancers · 2025Article
56 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
2 authors.
Funding
Abstract
introductionThe radiological reading room is undergoing a paradigm shift to a symbiosis of computer science and radiology using artificial intelligence integrated with machine and deep learning with radiomics to better define tissue characteristics. The goal is to use integrated deep learning and radiomics with radiological parameters to produce a personalized diagnosis for a patient. AREAS COVERED: This review provides an overview of historical and current deep learning and radiomics methods in the context of precision medicine in radiology. A literature search for 'Deep Learning', 'Radiomics', 'Machine learning', 'Artificial Intelligence', 'Convolutional Neural Network', 'Generative Adversarial Network', 'Autoencoders', Deep Belief Networks", Reinforcement Learning", and 'Multiparametric MRI' was performed in PubMed, ArXiv, Scopus, CVPR, SPIE, IEEE Xplore, and NIPS to identify articles of interest. EXPERT OPINION: In conclusion, both deep learning and radiomics are two rapidly advancing technologies that will unite in the future to produce a single unified framework for clinical decision support with a potential to completely revolutionize the field of precision medicine.
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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.