ReviewEuropean radiology experimental2020
Integrating radiomics into holomics for personalised oncology: from algorithms to bedside.
Review in European radiology experimental, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis 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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies.International journal of molecular sciences · 2022Pooled it
- Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.BMC medical imaging · 2025Article
- Development and validation of a radiomics model using plain radiographs to predict spine fractures with posterior wall injury.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Machine learning-assisted radiogenomic analysis for miR-15a expression prediction in renal cell carcinoma.BMC cancer · 2025Article
- Integrating ctDNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer.Cancer discovery · 2025Article
- Exploring radiomic features of lateral cerebral ventricles in postmortem CT for postmortem interval estimation.International journal of legal medicine · 2025Article
- Development of Clinical-Radiomics Nomogram for Predicting Post-Surgery Functional Improvement in High-Grade Glioma Patients.Cancers · 2025Article
- MRI management of focal liver lesions: what a beginner cannot fail to know.Frontiers in oncology · 2025Review
- Early and hereditary breast cancer: advances in risk stratification and imaging approaches.Therapeutic advances in medical oncology · 2025Review
- Review
- Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?Cancers · 2024Review
- Imaging biobanks: operational limits, medical-legal and ethical reflections.Frontiers in digital health · 2024Review
- Article
- Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram.BMC medical imaging · 2023Article
- Sensitivity of standardised radiomics algorithms to mask generation across different software platforms.Scientific reports · 2023Article
- Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer.The British journal of radiology · 2023Review
- Current Role of Delta Radiomics in Head and Neck Oncology.International journal of molecular sciences · 2023Review
- NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients.European radiology experimental · 2022Article
- Value assessment of artificial intelligence in medical imaging: a scoping review.BMC medical imaging · 2022Article
- Radiomic and Volumetric Measurements as Clinical Trial Endpoints-A Comprehensive Review.Cancers · 2022Review
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
Abstract
Radiomics, artificial intelligence, and deep learning figure amongst recent buzzwords in current medical imaging research and technological development. Analysis of medical big data in assessment and follow-up of personalised treatments has also become a major research topic in the area of precision medicine. In this review, current research trends in radiomics are analysed, from handcrafted radiomics feature extraction and statistical analysis to deep learning. Radiomics algorithms now include genomics and immunomics data to improve patient stratification and prediction of treatment response. Several applications have already shown conclusive results demonstrating the potential of including other "omics" data to existing imaging features. We also discuss further challenges of data harmonisation and management infrastructure to shed a light on the much-needed integration of radiomics and all other "omics" into clinical workflows. In particular, we point to the emerging paradigm shift in the implementation of big data infrastructures to facilitate databanks growth, data extraction and the development of expert software tools. Secured access, sharing, and integration of all health data, called "holomics", will accelerate the revolution of personalised medicine and oncology as well as expand the role of imaging specialists.
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.