ArticleTranslational cancer research2021
Diagnostic nomogram model for predicting preoperative pathological grade of meningioma.
Article in Translational cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas.BioMed research international · 2022Trial
- The public health burden of nervous system tumors in Anambra State, Nigeria necessitates strategic resource allocation.Scientific reports · 2026Article
- Grade-stratified meningioma risk among individuals who are non-Hispanic Black and interactions with male sex.Journal of the National Cancer Institute · 2025Article
- The joint impacts of sex and race/ethnicity on incidence of grade 1 versus grades 2-3 meningioma across the lifespan.Neuro-oncology advances · 2023Article
- The Performance of Different Machine Learning Algorithm and Regression Models in Predicting High-Grade Intracranial Meningioma.Brain sciences · 2023Article
- Meningiomas with CNS invasion.Frontiers in neuroscience · 2023Review
- Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network.PloS one · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMeningioma is the most common primary tumor of the central nervous system. Preoperative diagnosis of high-grade meningioma is helpful for the selection of treatment options. The aim of our study is to establish a diagnostic nomogram model for preoperative prediction of the pathological grade of meningioma.
methodsThe predictive model was established from a cohort of 215 clinicopathologically confirmed meningioma between January 2012 and December 2017. Radiomic features were collected from preoperative magnetic resonance imaging (MRI) and computed tomography of patients with meningioma. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction and feature selection. Multivariate logistic regression was used to build a predictive model and presented as a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was evaluated using bootstrapping validation.
resultsHigh-grade meningioma was observed in 47 patients (22%). The predictors included in the nomogram were tumor-brain interface, bone invasion, and tumor location. The final diagnostic model exhibited good calibration and discrimination with a C-index of 0.874 (95% confidence interval: 0.818-0.929) and a higher C-index of 0.868 in internal validation. Decision curve analysis (DCA) indicated that the nomogram is very useful in clinical practice.
conclusionsThis study provides a nomogram model with tumor-brain interface, bone invasion, and tumor location that can effectively predict the preoperative pathological grading of patients with meningioma and thus help clinicians provide more reasonable treatment strategies for meningioma patients.
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.