ReviewEuropean journal of hybrid imaging2018
Trends in oncologic hybrid imaging.
Review in European journal of hybrid imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Addressing unmet needs in nuclear medicine: Italian experience with PET/CT features and structured reporting in oncology.La Radiologia medica · 2026Article
- Breaking the Chains of Therapeutic Blockade: Pyroptosis-Induced Photothermal-Chemotherapy with Targeted Nanoprobes in Triple-Negative Breast Cancer.Biomaterials research · 2025Article
- In vivo fluorescence imaging: success in preclinical imaging paves the way for clinical applications.Journal of nanobiotechnology · 2022Review
- Multiplexed imaging in oncology.Nature biomedical engineering · 2022Review
- Transpathology: molecular imaging-based pathology.European journal of nuclear medicine and molecular imaging · 2021Review
- Current State of Breast Cancer Diagnosis, Treatment, and Theranostics.Pharmaceutics · 2021Review
- Maximizing the use of batch production ofNeuro-oncology practice · 2021Article
- Characterization with hybrid imaging of cystic pheochromocytomas: correlation with pathology.Quantitative imaging in medicine and surgery · 2021Article
- Reinventing Molecular Imaging with Total-Body PET, Part II: Clinical Applications.PET clinics · 2020Review
- Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: An overview of existing designs.Medical physics · 2020Review
- Radiotheranostics: a roadmap for future development.The Lancet. Oncology · 2020Review
- Multimodality Imaging in the Evaluation of Intracardiac Masses.Current treatment options in cardiovascular medicine · 2019Review
- Radiation dose in nuclear medicine: the hybrid imaging.La Radiologia medica · 2019Review
- The added value of whole-body magnetic resonance imaging in the management of patients with advanced breast cancer.PloS one · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hybrid imaging plays a central role in the diagnosis and management of a wide range of malignancies at all stages. In this article, we review the most pertinent historical developments, emerging clinical applications of novel radiotracers and imaging technologies, and potential implications for training and practice. This includes an overview of novel tracers for prostate, breast, and neuroendocrine tumors, assessment of tumor heterogeneity, the concept of image-guided 'biologically relevant dosing', and theranostic applications. Recent technological advancements, including time-of-flight PET, PET/MRI, and 'one-minute whole-body PET', are also covered. Finally, we discuss how these rapidly evolving applications might affect current training curricula and how imaging-derived big data could be harnessed to the benefit of our patients.
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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.