ArticleJournal of neuro-oncology2024
Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model.
Article in Journal of neuro-oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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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
4 citing papers in PubMed, 5 citations in OpenAlex.
- Prognostic significance of walking ability at hospital discharge for overall survival in newly diagnosed glioblastoma: a comparison across six peri-treatment assessments.Journal of neuro-oncology · 2026Article
- Conducting a randomized controlled clinical trial on palliative care in patients with glioblastoma - what are the challenges?Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- An unsupervised tool for biomarker discovery and cancer subtyping applied to glioblastoma.BioData mining · 2025Article
- Beyond Molecular Characterization: The Impact of Age-Adjusted Charlson Comorbidity Index in Glioblastoma Patients Treated with Radio or Radio-Chemotherapy.Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeGlioblastoma (GBM) is the most frequent glioma in adults with a high treatment resistance resulting into limited survival. The individual prognosis varies depending on individual prognostic factors, that must be considered while counseling patients with newly diagnosed GBM. The aim of this study was to elaborate a risk stratification algorithm based on reliable prognostic factors to facilitate a personalized prognosis estimation early on after diagnosis.
methodsA consecutive patient cohort with confirmed GBM treated between 2010 and 2021 was retrospectively analyzed. Clinical, radiological, and molecular parameters were assessed and included in the analysis. Overall survival (OS) was the primary outcome parameter. After identifying the strongest prognostic factors, a risk stratification algorithm was elaborated with estimated odds of survival.
resultsA total of 462 GBM patients were analyzed. The strongest prognostic factors were Charlson Comorbidity Index (CCI), extent of tumor resection, and adjuvant treatment. Patients with CCI ≤ 1 receiving tumor resection had the highest survival odds (88% for 10 months). On the contrary, patients with CCI > 3 receiving no adjuvant treatment had the lowest survival odds (0% for 10 months). The 10-months survival rate in patients with CCI > 3 receiving adjuvant treatment was 56% for patients younger than 70 years and 22% for patients older than 70 years.
conclusionA risk stratification algorithm based on significant prognostic factors allowed a personalized early prognosis estimation at the time of GBM diagnosis, that can contribute to a more personalized patient counseling.
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