ReviewJournal of translational medicine2019
Bringing radiomics into a multi-omics framework for a comprehensive genotype-phenotype characterization of oncological diseases.
Review in Journal of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 4 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
65 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Association of radiomic features with genomic signatures in thyroid cancer: a systematic review.Journal of translational medicine · 2024Pooled it
- Application of radiomics to meningiomas: A systematic review.Neuro-oncology · 2023Pooled it
- Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.Journal of medical Internet research · 2021Pooled it
- TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review.International journal of molecular sciences · 2019Pooled it
- Radiomics model based on multi-sequence MR images for predicting preoperative immunoscore in rectal cancer.La Radiologia medica · 2022Trial
- Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer.European journal of nuclear medicine and molecular imaging · 2022Trial
- Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images.BMC medical imaging · 2021Trial
- Radiomic analysis of medical imaging for classification in oncology and recommendations for clinically initiated research: a literature review.Quantitative imaging in medicine and surgery · 2026Review
- Current Trends and Future Prospects of Radiomics and Machine Learning (ML) Models in Spinal Tumors-A Narrative Review.Journal of imaging · 2026Review
- An interpretable machine learning model integrating clinical and CT radiomic features for the detection of interstitial lung disease in idiopathic inflammatory myopathies.Arthritis research & therapy · 2026Article
- The GSA Family in 2025: A Broadened Sharing Platform for Multi-omics and Multimodal Data.Genomics, proteomics & bioinformatics · 2025Article
- Emerging biomarkers for early cancer detection and diagnosis: challenges, innovations, and clinical perspectives.European journal of medical research · 2025Review
- Review
- Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- Radiomics to predict tumor response to combination chemoradiotherapy in squamous cell carcinoma of the anal canal: a preliminary investigation.European radiology experimental · 2025Article
- Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features.Frontiers in oncology · 2025Article
- Research trends of artificial intelligence and radiomics in lung cancer: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer.BMC medical imaging · 2024Article
- Multiomics-Based Outcome Prediction in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR).Cancers · 2024Article
5 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic and radiomic data integration, namely radiogenomics, can provide meaningful knowledge in cancer diagnosis, prognosis and treatment. Despite several data structures based on multi-layer architecture proposed to combine multi-omic biological information, none of these has been designed and assessed to include radiomic data as well. To meet this need, we propose to use the MultiAssayExperiment (MAE), an R package that provides data structures and methods for manipulating and integrating multi-assay experiments, as a suitable tool to manage radiogenomic experiment data. To this aim, we first examine the role of radiogenomics in cancer phenotype definition, then the current state of radiogenomics data integration in public repository and, finally, challenges and limitations of including radiomics in MAE, designing an extended framework and showing its application on a case study from the TCGA-TCIA archives. Radiomic and genomic data from 91 patients have been successfully integrated in a single MAE object, demonstrating the suitability of the MAE data structure as container of radiogenomic data.
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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.