Despite advances in the therapy of Central Nervous System (CNS) malignancies, treatment of glioblastoma (GB) poses significant challenges due to GB resistance and high recurrence rates following post-operative radiochemotherapy. The majority of prognostic and predictive GB biomarkers are currently developed using tumour samples obtained through surgical interventions. However, the selection criteria adopted by different neurosurgeons to determine which cases are suitable for surgery make operated patients not representative of all GB cases. Particularly, geriatric and frail individuals are excluded from surgical consideration in some cancer centers. Such selection generates a survival (or selection) bias that introduces limitations, rendering the patients or data chosen for downstream analyses not representative of the entire community. In this review, we discuss the implication of survivorship bias on current and novel biomarkers for patient selection, stratification, therapy, and outcome analyses.
The impact of survivorship bias in glioblastoma research
Caffo, MariaMembro del Collaboration Group
;
2023-01-01
Abstract
Despite advances in the therapy of Central Nervous System (CNS) malignancies, treatment of glioblastoma (GB) poses significant challenges due to GB resistance and high recurrence rates following post-operative radiochemotherapy. The majority of prognostic and predictive GB biomarkers are currently developed using tumour samples obtained through surgical interventions. However, the selection criteria adopted by different neurosurgeons to determine which cases are suitable for surgery make operated patients not representative of all GB cases. Particularly, geriatric and frail individuals are excluded from surgical consideration in some cancer centers. Such selection generates a survival (or selection) bias that introduces limitations, rendering the patients or data chosen for downstream analyses not representative of the entire community. In this review, we discuss the implication of survivorship bias on current and novel biomarkers for patient selection, stratification, therapy, and outcome analyses.File | Dimensione | Formato | |
---|---|---|---|
Caffo_Impact_2023.pdf
solo utenti autorizzati
Descrizione: Articolo principale
Tipologia:
Versione Editoriale (PDF)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
1.11 MB
Formato
Adobe PDF
|
1.11 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.