ReviewCurrent oncology reports2017
What's New in Imaging for Gynecologic Cancer?
Review in Current oncology reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Role of MRI in diagnosing the primary site of origin in indeterminate cases of uterocervical carcinomas: a systematic review and meta-analysis.The British journal of radiology · 2022Pooled it
- Diagnostic Value of MRI for Pelvic Lymph Node Extracapsular Invasion in Early-Stage Endometrial Carcinoma.International journal of women's health · 2025Article
- Distribution of prostate specific membrane antigen (PSMA) on PET-MRI in patients with and without ovarian cancer.Abdominal radiology (New York) · 2023Article
- Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis.Frontiers in oncology · 2023Review
- Dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian tumor categorization: comparison with apparent diffusion coefficient histogram analysis and the tumor cell proliferation marker.American journal of translational research · 2023Article
- Comparison of a deep learning-accelerated T2-weighted turbo spin echo sequence and its conventional counterpart for female pelvic MRI: reduced acquisition times and improved image quality.Insights into imaging · 2022Article
- Review
- Diagnostic value of integratedBMC cancer · 2022Article
- PET/MR imaging in gynecologic cancer: tips for differentiating normal gynecologic anatomy and benign pathology versus cancer.Abdominal radiology (New York) · 2022Review
- An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer.Journal of clinical medicine · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance imaging (MRI) is the optimal modality for local staging of gynecological tumors. Advances in functional MRI with diffusion-weighted and dynamic contrast-enhanced sequences provide more detailed information regarding tumor cellularity, vascularity, and viability. Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) now has an established role in imaging for gynecological cancers, particularly staging of locally advanced cervical cancers and pre-salvage exenterative therapy in relapsed gynecologic tumors. Novel PET tracers, targeting other aspects of tumor biology, are being evaluated although none are currently in routine clinical use. New PET/MR scanners have the potential to combine the strengths of both modalities in one sitting. This review covers advances in gynecologic imaging concentrating on cervical, endometrial, and ovarian cancers.
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.