ArticleDiscover oncology2025
Designing a web-based platform for dynamic estimation of individualized conditional survival in grade 3 gliomas.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- How survival time reshapes prognostic risk in giant cell glioblastoma.Discover oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGrade 3 gliomas, including anaplastic astrocytoma and anaplastic oligodendroglioma, are aggressive brain tumors with heterogeneous prognoses. Existing survival models often fail to account for time-dependent changes in risk, limiting their utility in long-term survivorship planning. This study aimed to characterize dynamic survival trends and construct a conditional survival (CS)-based nomogram to facilitate individualized risk prediction.
methodsData from 7278 patients with histologically confirmed grade 3 glioma were extracted from the SEER database (2000-2021). CS and annual hazard rate analyses were conducted to assess time-varying survival probabilities. Prognostic variables were selected using best subset regression (BSR), least absolute shrinkage and selection operator (LASSO), and stepwise regression methods. A multivariable Cox model incorporating key predictors was used to construct a CS-nomogram, which was validated using calibration curves, time-dependent ROC analysis, and decision curve analysis (DCA). A risk stratification system was developed, and an interactive web-based survival calculator was created for clinical application.
resultsCS analysis revealed that 10-year survival probability increased from 29% at diagnosis to 67% after surviving 5 years. The annual hazard rate declined from 29.06% in the first year to 1.8% by the tenth year. Seven independent prognostic factors-age, tumor site, histology, tumor stage, surgery, radiotherapy, and chemotherapy-were incorporated into the final CS-nomogram, which demonstrated strong discrimination (AUCs for 3-, 5-, and 10-year survival > 0.75) and excellent calibration. Risk scores derived from the model effectively stratified patients into high- and low-risk groups with significantly different survival outcomes (P < 0.001). An online calculator was developed to enable real-time, patient-specific survival prediction.
conclusionThis study provided a comprehensive assessment of dynamic survival patterns in grade 3 glioma and introduced a validated CS-based nomogram and web-based tool for personalized prognostic evaluation. These resources offer valuable guidance for treatment planning and long-term follow-up in clinical practice.
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.