ArticleFrontiers in oncology2021
Preoperative Prediction of Lymphovascular Space Invasion in Cervical Cancer With Radiomics -Based Nomogram.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 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
19 citing papers in PubMed, 3 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
- Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Prediction of Postoperative Pathologic Risk Factors in Cervical Cancer Patients Treated with Radical Hysterectomy by Machine Learning.Current oncology (Toronto, Ont.) · 2022Trial
- Peritumoral ultrasound-driven multimodal AI model for predicting lymphovascular space invasion and prognosis in cervical cancer.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
- Modification of Pathological Nodal Classification for pT1b Esophageal Squamous Cell Carcinoma With Lymphovascular Invasion: Over 10-Year Experience.Cancer reports (Hoboken, N.J.) · 2025Article
- Nomogram prediction of the lymph-vascular space invasion in cervical cancer: comparison of 2009 and 2018 staging systems.Frontiers in oncology · 2025Article
- Risk factors for pelvic lymph node metastasis in cervical cancer: a retrospective analysis of 186 patients.Frontiers in oncology · 2025Article
- Preoperative prediction of recurrence risk factors in operable cervical cancer based on clinical-radiomics features.Frontiers in oncology · 2025Article
- Predicting lymphovascular space invasion in early-stage cervical squamous cell carcinoma using heart rate variability.Frontiers in oncology · 2025Article
- Multiparametric magnetic resonance imaging-based comprehensive model on prediction of lymphovascular space invasion in cervical cancer.Frontiers in oncology · 2025Article
- Multiparametric mri-based radiomics nomogram for predicting lymph-vascular space invasion in cervical cancer.BMC medical imaging · 2024Article
- A nomogram of preoperative indicators predicting lymph vascular space invasion in cervical cancer.Archives of gynecology and obstetrics · 2024Article
- Profiling of Lymphovascular Space Invasion in Cervical Cancer Revealed PI3K/Akt Signaling Pathway Overactivation and Heterogenic Tumor-Immune Microenvironments.Life (Basel, Switzerland) · 2023Article
- A nomogram model based on pre-treatment and post-treatment MR imaging radiomics signatures: application to predict progression-free survival for nasopharyngeal carcinoma.Radiation oncology (London, England) · 2023Article
- Development and validation of a radiomics-based nomogram for the prediction of postoperative malnutrition in stage IB1-IIA2 cervical carcinoma.Frontiers in nutrition · 2023Article
- MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer.The British journal of radiology · 2022Article
- Development and validation of a prognostic nomogram for 2018 FIGO stages IB1, IB2, and IIA1 cervical cancer: a large multicenter study.Annals of translational medicine · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo build and evaluate a radiomics-based nomogram that improves the predictive performance of the LVSI in cervical cancer non-invasively before the operation.
methodThis study involved 149 patients who underwent surgery with cervical cancer from February 2017 to October 2019. Radiomics features were extracted from T2 weighted imaging (T2WI). The radiomic features were selected by logistic regression with the least absolute shrinkage and selection operator (LASSO) penalty in the training cohort. Based on the selected features, support vector machine (SVM) algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinical risk factors, the radiomics-based nomogram was developed. The sensitivity, specificity, accuracy, and area under the curve (AUC) and Receiver operating characteristic (ROC) curve were calculated to assess these models.
resultThe radiomics model performed much better than the clinical model in both training (AUCs 0.925
conclusionRadiomics-based nomogram was a useful tool for predicting LVSI of cervical cancer. This would aid the selection of the optimal therapeutic strategy and clinical decision-making for individuals.
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.