ReviewTaehan Yongsang Uihakhoe chi2020
[Current Applications and Future Perspectives of Brain Tumor Imaging].
Review in Taehan Yongsang Uihakhoe chi, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Time optimization of gadobutrol-enhanced brain MRI for metastases and primary tumors using a dynamic contrast-enhanced imaging.BMC medical imaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anatomical imaging is the basis of the diagnosis and treatment response assessment of brain tumors. Among the existing imaging techniques currently available in clinical practice, diffusion-weighted imaging and perfusion imaging provide additional information. Recently, with the increasing importance of evaluation of the genomic variation and heterogeneity of tumors, clinical application of imaging techniques using radiomics and deep learning is expected. In this review, we will describe recommendations for magnetic resonance imaging protocols focusing on anatomical images that are still important in the clinical application of brain tumor imaging, and the basic principles of diffusion-weighted imaging and perfusion imaging among the advanced imaging techniques, as well as their pathophysiological background and clinical application. Finally, we will review the future perspectives of radiomics and deep learning applications in brain tumor imaging, which have been studied to a great extent due to the development of computer technology.
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.