ReviewFrontiers in neurology2020
PET/MRI Radiomics in Patients With Brain Metastases.
Review in Frontiers in neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Update on the Use of PET/MRI Contrast Agents and Tracers in Brain Oncology: A Systematic Review.International journal of nanomedicine · 2022Pooled it
- PET-based Radiomics Analysis for Predicting Prognosis and Differentiation Treatment-Related Changes in Glioma: A Systematic Review.Journal of biomedical physics & engineering · 2026Article
- Article
- The use of PET/MRI in radiotherapy.Insights into imaging · 2024Review
- Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases.Clinical & experimental metastasis · 2024Article
- Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data.Nuklearmedizin. Nuclear medicine · 2023Review
- Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.NPJ precision oncology · 2023Review
- Review
- Impact of framing scheme optimization and smoking status on binding potential analysis in dynamic PET with [EJNMMI research · 2023Article
- Phantom Study on the Robustness of MR Radiomics Features: Comparing the Applicability of 3D Printed and Biological Phantoms.Diagnostics (Basel, Switzerland) · 2022Article
- PET/MRI and PET/CT Radiomics in Primary Cervical Cancer: A Pilot Study on the Correlation of Pelvic PET, MRI, and CT Derived Image Features.Molecular imaging and biology · 2022Article
- Brain metastases: the role of clinical imaging.The British journal of radiology · 2022Review
- Review of Current Principles of the Diagnosis and Management of Brain Metastases.Frontiers in oncology · 2022Review
- Differentiating Glioblastoma Multiforme from Brain Metastases Using Multidimensional Radiomics Features Derived from MRI and Multiple Machine Learning Models.BioMed research international · 2022Article
- The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI.Cancers · 2021Article
- A deep look into radiomics.La Radiologia medica · 2021Review
- Artificial intelligence for molecular neuroimaging.Annals of translational medicine · 2021Review
- Brain metastases of lung cancer: comparison of survival outcomes among whole brain radiotherapy, whole brain radiotherapy with consecutive boost, and simultaneous integrated boost.Journal of cancer research and clinical oncology · 2021Article
- TERT-Promoter Mutational Status in Glioblastoma - Is There an Association With Amino Acid Uptake on DynamicFrontiers in oncology · 2021Article
- Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges.Neuro-oncology advances · 2020Article
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
Although a variety of imaging modalities are used or currently being investigated for patients with brain tumors including brain metastases, clinical image interpretation to date uses only a fraction of the underlying complex, high-dimensional digital information from routinely acquired imaging data. The growing availability of high-performance computing allows the extraction of quantitative imaging features from medical images that are usually beyond human perception. Using machine learning techniques and advanced statistical methods, subsets of such imaging features are used to generate mathematical models that represent characteristic signatures related to the underlying tumor biology and might be helpful for the assessment of prognosis or treatment response, or the identification of molecular markers. The identification of appropriate, characteristic image features as well as the generation of predictive or prognostic mathematical models is summarized under the term radiomics. This review summarizes the current status of radiomics in patients with brain metastases.
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.