ArticleActa radiologica open2017
MRI texture features may predict differentiation and nodal stage of cervical cancer: a pilot study.
Article in Acta radiologica open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncology.Frontiers in medicine · 2024Pooled it
- Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study.Scientific reports · 2025Article
- An Update on the Role of MRI in Treatment Stratification of Patients with Cervical Cancer.Cancers · 2023Review
- Texture Analysis in Uterine Cervix Carcinoma: Primary Tumour and Lymph Node Assessment.Diagnostics (Basel, Switzerland) · 2023Article
- A feasibility study of reduced full-of-view synthetic high-b-value diffusion-weighted imaging in uterine tumors.Insights into imaging · 2023Article
- Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification.European radiology experimental · 2022Article
- MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- Post treatment imaging in patients with local advanced cervical carcinoma.Frontiers in oncology · 2022Review
- Radiomics in cervical and endometrial cancer.The British journal of radiology · 2021Review
- Textural differences based on apparent diffusion coefficient maps for discriminating pT3 subclasses of rectal adenocarcinoma.World journal of clinical cases · 2021Article
- Impact of different scanners and acquisition parameters on robustness of MR radiomics features based on women's cervix.Scientific reports · 2020Article
- MRI texture features differentiate clinicopathological characteristics of cervical carcinoma.European radiology · 2020Article
- Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images.European radiology · 2020Article
- Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram.European radiology · 2020Article
- Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps.World journal of gastroenterology · 2020Observational
- Texture Analysis of Apparent Diffusion Coefficient Maps in Cervical Carcinoma: Correlation with Histopathologic Findings and Prognosis.Radiology. Imaging cancer · 2020Article
- Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis.Radiation oncology (London, England) · 2020Article
- Diffusion-weighted magnetic resonance imaging of primary cervical cancer in the detection of sub-centimetre metastatic lymph nodes.Cancer imaging : the official publication of the International Cancer Imaging Society · 2020Article
- B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer.Korean journal of radiology · 2020Article
- Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer.Frontiers in oncology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTexture analysis in oncological magnetic resonance imaging (MRI) may yield surrogate markers for tumor differentiation and staging, both of which are important factors in the treatment planning for cervical cancer. PURPOSE: To identify texture features which may predict tumor differentiation and nodal status in diffusion-weighted imaging (DWI) of cervical carcinoma. MATERIAL AND
methodsTwenty-three patients were enrolled in this prospective, institutional review board (IRB)-approved study. Pelvic MRI was performed at 3-T including a DWI echo-planar sequence with b-values 40, 300, and 800 s/mm
resultsMean age was 55 years (range = 37-78 years). Biopsy revealed two well-differentiated, eight moderately differentiated, two moderately to poorly differentiated tumors, and five poorly differentiated tumors. Six tumors could not be graded. Lymph nodes were involved in 11 patients. Three GLM features correlated with the differentiation: LRHGE (ϱ = 0.53,
conclusionWe identified potentially predictive GLM features for histological tumor differentiation and histogram features for nodal cancer stage.
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.