ArticleIEEE access : practical innovations, open solutions2021
Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies.IEEE open journal of engineering in medicine and biology · 2025Article
- Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning.Physics and imaging in radiation oncology · 2025Article
- Synthetic CT generation for pelvic cases based on deep learning in multi-center datasets.Radiation oncology (London, England) · 2024Article
- Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.Scientific reports · 2024Article
- Artificial intelligence applications in prostate cancer.Prostate cancer and prostatic diseases · 2024Review
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- Article
- Synthetic artificial intelligence using generative adversarial network for retinal imaging in detection of age-related macular degeneration.Frontiers in medicine · 2023Article
- Abdominopelvic MR to CT registration using a synthetic CT intermediate.Journal of applied clinical medical physics · 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
14 authors.
Funding
Abstract
Multi-modality imaging constitutes a foundation of precision medicine, especially in oncology where reliable and rapid imaging techniques are needed in order to insure adequate diagnosis and treatment. In cervical cancer, precision oncology requires the acquisition of
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.