ArticleJournal of magnetic resonance imaging : JMRI2019
MR-Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph-Vascular Space Invasion preoperatively.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 4 of them syntheses 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
61 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Imaging-Based AI for Predicting Lymphovascular Space Invasion in Cervical Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- The role of radiomics for predicting of lymph-vascular space invasion in cervical cancer patients based on artificial intelligence: a systematic review and meta-analysis.Journal of gynecologic oncology · 2025Pooled it
- Radiomics based on MRI in predicting lymphovascular space invasion of cervical cancer: a meta-analysis.Frontiers in oncology · 2024Pooled it
- Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Multiparametric MRI radiomics nomogram for predicting lymph-vascular space invasion in early-stage cervical cancer.The British journal of radiology · 2022Trial
- Peritumoral ultrasound-driven multimodal AI model for predicting lymphovascular space invasion and prognosis in cervical cancer.Abdominal radiology (New York) · 2026Article
- Intravoxel incoherent motion imaging with improved image quality by deep learning reconstruction for prediction of lymph-vascular space invasion in patients with cervical cancer.Abdominal radiology (New York) · 2026Article
- Preoperative prediction of parametrial invasion in early-stage cervical cancer: a radiomics nomogram fusing multi-parametric MRI and clinical biomarkers.Abdominal radiology (New York) · 2026Article
- Preoperative prediction of cervical cancer recurrence using explainable MRI radiomics: a SHAP-Guided machine learning study.Abdominal radiology (New York) · 2026Article
- An interpretable MRI radiomics approach for preoperative prediction of lymphovascular space invasion in cervical cancer using optimal peritumoral region.Frontiers in oncology · 2026Article
- Clinical-radiomics nomogram construction from magnetic resonance imaging to diagnose osteoporosis: a preliminary study.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- 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
- Deep transfer learning radiomics for distinguishing sinonasal malignancies: a preliminary MRI study.Future oncology (London, England) · 2025Article
- Multi-parametric MRI-based radiomics nomogram for predicting lymphovascular space invasion in early-stage cervical adenocarcinoma.Frontiers in oncology · 2025Article
- Multiparametric magnetic resonance imaging-based comprehensive model on prediction of lymphovascular space invasion in cervical cancer.Frontiers in oncology · 2025Article
- Predicting lymphovascular space invasion in early-stage cervical squamous cell carcinoma using heart rate variability.Frontiers in oncology · 2025Article
- Nomogram prediction of the lymph-vascular space invasion in cervical cancer: comparison of 2009 and 2018 staging systems.Frontiers in oncology · 2025Article
- Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI.Journal of applied clinical medical physics · 2024Article
- Multiparametric mri-based radiomics nomogram for predicting lymph-vascular space invasion in cervical cancer.BMC medical imaging · 2024Article
- Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.BMC women's health · 2024Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLymph-vascular space invasion (LVSI) is an unfavorable prognostic factor in cervical cancer. Unfortunately, there are no current clinical tools for the preoperative prediction of LVSI. PURPOSE: To develop and validate an axial T STUDY TYPE: Retrospective. POPULATION: In all, 105 patients were randomly divided into two cohorts at a 2:1 ratio. FIELD STRENGTH/SEQUENCE: T ASSESSMENT: Univariate analysis was performed on the radiomics features and clinical parameters. Multivariate analysis was performed to determine the optimal feature subset. The receiver operating characteristic (ROC) analysis was performed to evaluate the performance of prediction model and radiomics nomogram. STATISTICAL TESTS: The Mann-Whitney U-test and the chi-square test were used to evaluate the performance of clinical characteristics and LVSI status by pathology. The minimum-redundancy/maximum-relevance and recursive feature elimination methods were applied to select the features. The radiomics model was constructed using logistic regression.
resultsThree radiomics features and one clinical characteristic were selected. The radiomics nomogram showed favorable discrimination between LVSI and non-LVSI groups. The AUC was 0.754 (95% confidence interval [CI], 0.6326-0.8745) in the training cohort and 0.727 (95% CI, 0.5449-0.9097) in the validation cohort. The specificity and sensitivity were 0.756 and 0.828 in the training cohort and 0.773 and 0.692 in the validation cohort. DATA
conclusionT LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1420-1426.
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.