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Journal of Neuro-Oncology

Open Access 19-04-2024 | Glioblastoma | Research

Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model

Authors: Katharina Conrad, Ronja Löber-Handwerker, Mohammad Hazaymeh, Veit Rohde, Vesna Malinova

Published in: Journal of Neuro-Oncology

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Abstract

Purpose

Glioblastoma (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.

Methods

A 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.

Results

A 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.

Conclusion

A 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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Metadata
Title
Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model
Authors
Katharina Conrad
Ronja Löber-Handwerker
Mohammad Hazaymeh
Veit Rohde
Vesna Malinova
Publication date
19-04-2024
Publisher
Springer US
Published in
Journal of Neuro-Oncology
Print ISSN: 0167-594X
Electronic ISSN: 1573-7373
DOI
https://doi.org/10.1007/s11060-024-04683-6