ReviewCancers2019
Value of Three-Dimensional Imaging Systems for Image-Guided Carbon Ion Radiotherapy.
Review in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed.
- Evaluation and implementation of an independent Kilovoltage X-ray-based imaging platform for carbon ion radiotherapy.Journal of applied clinical medical physics · 2026Article
- Bone matching versus tumor matching in image-guided carbon ion radiotherapy for locally advanced non-small cell lung cancer.Radiation oncology (London, England) · 2024Article
- Possibilities and challenges when using synthetic computed tomography in an adaptive carbon-ion treatment workflow.Zeitschrift fur medizinische Physik · 2023Article
- Value of carbon-ion radiotherapy for early stage non-small cell lung cancer.Clinical and translational radiation oncology · 2022Review
- Ways to unravel the clinical potential of carbon ions for head and neck cancer reirradiation: dosimetric comparison and local failure pattern analysis as part of the prospective randomized CARE trial.Radiation oncology (London, England) · 2022Article
- Streamlining the image-guided radiotherapy process for proton beam therapy.The British journal of radiology · 2021Article
- Adaptive planning based on single beam optimization in passive scattering carbon ion radiotherapy for patients with pancreatic cancer.Radiation oncology (London, England) · 2021Article
- Future Developments in Charged Particle Therapy: Improving Beam Delivery for Efficiency and Efficacy.Frontiers in oncology · 2021Review
- Robust Angle Selection in Particle Therapy.Frontiers in oncology · 2021Review
- Dosimetric Impact of Inter-Fraction Anatomical Changes in Carbon Ion Boost Treatment for High-Risk Prostate Cancer (AIRC IG 14300).Frontiers in oncology · 2021Article
- Impact of Inter-fractional Anatomical Changes on Dose Distributions in Passive Carbon-Ion Radiotherapy for Prostate Cancer: Comparison of Vertical and Horizontal Fields.Frontiers in oncology · 2020Article
- Evaluation of Intensity- and Contour-Based Deformable Image Registration Accuracy in Pancreatic Cancer Patients.Cancers · 2019Article
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
Carbon ion radiotherapy (C-ion RT) allows excellent dose distribution because of the Bragg Peak. Compared with conventional radiotherapy, it delivers a higher dose with a smaller field. However, the dose distribution is sensitive to anatomical changes. Imaging technologies are necessary to reduce uncertainties during treatment, especially for hypofractionated and adaptive radiotherapy (ART). In-room computed tomography (CT) techniques, such as cone-beam CT (CBCT) and CT-on-rails are routinely used in photon centers and play a key role in improving treatment accuracy. For C-ion RT, there is an increasing demand for a three-dimensional (3D) image-guided system because of the limitations of the present two-dimensional (2D) imaging verification technology. This review discusses the current imaging system used in carbon ion centers and the potential benefits of a volumetric image-guided system.
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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.