ReviewTheranostics2019
The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges.
Review in Theranostics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 529 papers, 12 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
529 citing papers in PubMed, 12 syntheses or guidelines pooled it.
- Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Diagnostic performance of radiomics for detecting and characterising upper tract urothelial carcinoma (UTUC): a systematic review.World journal of urology · 2026Pooled it
- Role of Radiomics to Predict Disease Recurrence in Sinonasal Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery · 2026Pooled it
- Application and integration of deep learning in tumour radiomics: bibliometrics and visualisation analysis.Frontiers in artificial intelligence · 2026Pooled it
- Causal inference in the diagnosis and prognosis of ovarian cancer: current state and future directions.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Pooled it
- Convolutional neural network models of structural MRI for discriminating categories of cognitive impairment: a systematic review and meta-analysis.BMC neurology · 2025Pooled it
- Diagnostic Performance of Computed Tomography-Based Artificial Intelligence for Early Recurrence of Cholangiocarcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- 18F-FDG PET/CT Semiquantitative and Radiomic Features for Assessing Pathologic Axillary Lymph Node Status in Clinical Stage I-III Breast Cancer Patients: A Systematic Review.Current oncology (Toronto, Ont.) · 2025Pooled it
- MRI radiomics in diagnosing high and low grade meningiomas through systematic review and meta analysis.Scientific reports · 2025Pooled it
- Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer.European radiology · 2025Pooled it
- Quality and accuracy of radiomics models in predicting KRAS status in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions.Journal of cancer research and clinical oncology · 2024Pooled it
- A Novel Tumor Burden Index to Predict the Prognosis in Stage IA Lung Adenocarcinoma.Annals of surgical oncology · 2026Article
- Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size.European radiology · 2026Article
- Ultrasound Radiomics for Hashimoto's Thyroiditis: State of the Art and Future Perspectives.Journal of clinical ultrasound : JCU · 2026Review
- The Challenges of AI Applications in Health Care: Qualitative Study.Journal of medical Internet research · 2026Article
- Bibliometric visualization analysis of the application of artificial intelligence in gastrointestinal surgery recent 10 years.Updates in surgery · 2026Review
- Chinese expert concern and consensus on applications of artificial intelligence in clinical cancer imaging.Insights into imaging · 2026Article
- B-Mode Ultrasound Radiomics for Differentiating Benign and Malignant Small Hyperechoic Renal Masses: An Exploratory Single-Center Experience.Journal of imaging · 2026Article
- Impact of Different Energy Levels of Virtual Monoenergetic Reconstructions on Radiomic Features Stability in Organic Phantom Imaging Using Photon-Counting CT.Tomography (Ann Arbor, Mich.) · 2026Article
469 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.
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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.