Department of Oncology, University of Oxford, Oxford, UK; Radiation Oncology, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy.
Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
Crit Rev Oncol Hematol. 2023 Aug;188:104065. doi: 10.1016/j.critrevonc.2023.104065. Epub 2023 Jun 29.
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 radio-chemotherapy. 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.
尽管中枢神经系统 (CNS) 恶性肿瘤的治疗取得了进展，但由于胶质母细胞瘤 (GB) 的耐药性和术后放化疗后的高复发率，其治疗仍面临重大挑战。目前大多数用于预测和预后的 GB 生物标志物是使用通过手术干预获得的肿瘤样本开发的。然而，不同神经外科医生采用的选择标准来确定哪些病例适合手术，使得接受手术的患者不能代表所有的 GB 病例。特别是在一些癌症中心，老年和体弱的个体被排除在手术考虑之外。这种选择产生了生存（或选择）偏差，引入了局限性，使得选择用于下游分析的患者或数据不能代表整个群体。在这篇综述中，我们讨论了生存偏差对目前和新的生物标志物在患者选择、分层、治疗和结果分析中的影响。
