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

Pasqualetti, Francesco
Primo
;
Montemurro, Nicola;Mazzanti, Chiara Maria;Paiar, Fabiola;Bocci, Guido;Lombardi, Giuseppe;
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.
2023
Pasqualetti, Francesco; Barberis, Alessandro; Zanotti, Sofia; Montemurro, Nicola; De Salvo, Gian Luca; Soffietti, Riccardo; Mazzanti, Chiara Maria; Iu...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1200410
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