Trial reportBMC cancer2023
Development of an integrated predictive model for postoperative glioma-related epilepsy using gene-signature and clinical data.
Trial report in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- The prevalence of post-therapy epilepsy in patients treated for high-grade glial tumors: a systematic review and meta-analysis.Medical oncology (Northwood, London, England) · 2025Pooled it
- An Interpretable Multimodal Machine-Learning Model for Non-Invasive Preoperative Glioma Grading.Cancers · 2026Article
- Tumor-associated epilepsy and high expression of xCT shape the proteome of IDH-wildtype glioblastoma.Cell death discovery · 2026Article
- Glioma-related epilepsy in patients with newly diagnosed and recurrent glioblastoma, IDH-wildtype: a study under the 2021 WHO CNS tumor classification.BMC cancer · 2025Article
- Epilepsy as a dynamic disease: Toward actionable, individualized seizure risk prediction.Epilepsia · 2025Review
- Machine learning-based models for predicting glioma-associated epilepsy: a systematic review and meta-analysis.Discover oncology · 2025Article
- The molecular landscape of glioblastoma-associated epilepsy.Journal of molecular medicine (Berlin, Germany) · 2025Review
- Leveraging pathological markers of lower grade glioma to predict the occurrence of secondary epilepsy, a retrospective study.Scientific reports · 2025Article
- Prognostic models for seizures and epilepsy after stroke, tumors and traumatic brain injury.Clinical neurophysiology practice · 2025Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to develop an integrated model for predicting the occurrence of postoperative seizures in patients with diffuse high-grade gliomas (DHGGs) using clinical and RNA-seq data.
methodsPatients with DHGGs, who received prophylactic anti-epileptic drugs (AEDs) for three months following surgery, were enrolled into the study. The patients were assigned randomly into training (n = 166) and validation (n = 42) cohorts. Differentially expressed genes (DEGs) were identified based on preoperative glioma-related epilepsy (GRE) history. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to construct a predictive gene-signature for the occurrence of postoperative seizures. The final integrated prediction model was generated using the gene-signature and clinical data. Receiver operating characteristic analysis and calibration curve method were used to evaluate the accuracy of the gene-signature and prediction model using the training and validation cohorts.
resultsA seven-gene signature for predicting the occurrence of postoperative seizures was developed using LASSO logistic regression analysis of 623 DEGs. The gene-signature showed satisfactory predictive capacity in the training cohort [area under the curve (AUC) = 0.842] and validation cohort (AUC = 0.751). The final integrated prediction model included age, temporal lobe involvement, preoperative GRE history, and gene-signature-derived risk score. The AUCs of the integrated prediction model were 0.878 and 0.845 for the training and validation cohorts, respectively.
conclusionWe developed an integrated prediction model for the occurrence of postoperative seizures in patients with DHGG using clinical and RNA-Seq data. The findings of this study may contribute to the development of personalized management strategies for patients with DHGGs and improve our understanding of the mechanisms underlying GRE in these patients.
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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.