ArticleFrontiers in oncology2021
A Novel Nomogram for Predicting the Risk of Short-Term Recurrence After Surgery in Glioma Patients.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- Gross Total Resection and Recurrence-Free Survival in Diffuse Gliomas: A Single-Center Cohort Study from Eastern Europe.Clinics and practice · 2026Article
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Risk factors and a new nomogram for glioblastoma: based on a retrospective study.Frontiers in immunology · 2025Article
- Risk factors, prognostic potency, and longitudinal variation of anxiety and depression in postoperative glioma patients.Frontiers in surgery · 2022Article
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13 authors.
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Abstract
objectiveThe aim of this study was to establish a nomogram model for predicting the risk of short-term recurrence in glioma patients.
methodsThe clinical data of recurrent glioma patients were summarized and analyzed in this study. Univariate and multivariate logistic regression analyses were performed to analyze the correlation between clinical data and the risk of short-term recurrence after operation. A nomogram was established based on the multivariate logistic regression model results.
resultsA total of 175 patients with recurrent glioma were enrolled, with 53 patients in the short-term recurrence (STR) group (recurrent time ≤6 months) and 122 patients in the long-term recurrence (LTR) group (recurrent time ≥36 months). Univariate analysis revealed that age at diagnosis, Karnofsky performance scores (KPSs), tumor location, glioma grade, glioma type, extent of resection (EOR), adjuvant chemotherapy (ad-CT), concurrent chemotherapy (co-CT), and isocitrate dehydrogenase (IDH) status were significantly associated with the short-term glioma recurrence. Multivariate analyses revealed that age at diagnosis, KPS, glioma grade, EOR, and IDH were independent risk factors for short-term glioma recurrence. A risk nomogram for the short-term recurrence of glioma was established, with the concordance index (C-index) of 0.971. The findings of calibration and receiver operating characteristic (ROC) curves showed that our nomogram model had good performance and discrimination to estimate short-term recurrence probability.
conclusionThis nomogram model provides reliable information about the risk of short-term glioma recurrence for oncologists and neurosurgeons. This model can predict the short-term recurrence probability and give assistance to decide the interval of follow-up or formulate individualized treatment strategies based on the predicted results. A free online prediction risk tool for this nomogram is provided: https://rj2021.shinyapps.io/Nomogram_ recurrence-risk/.
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