Letter to the Editor: Biases in estimation of overall survival in patients who underwent repeat resection of glioblastoma

A Meta-Analysis of Survival Outcomes Following Reoperation in Recurrent Glioblastoma: Time to Consider the Timing of Reoperation

Background: Glioblastoma multiforme (GBM) inevitably recurs, but no standard regimen has been established for recurrent patients. Reoperation at recurrence alleviates mass effects, and the survival benefit has been reported in many studies. However, in most studies, the effect of reoperation timing on survival benefit was ignored. The aim of this meta-analysis was to investigate whether reoperation provided similar survival benefits in recurrent GBM patients when it was analyzed as a fixed or time-dependent covariate.

Methods: A systematic literature search of PubMed, EMBASE, and Cochrane databases was performed to identify original articles that evaluated the associations between reoperation and prognosis in recurrent GBM patients.

Results: Twenty-one articles involving 8,630 patients were included. When reoperation was considered as a fixed covariate, it was associated with better overall survival (OS) and post-progression survival (PPS) (OS: HR = 0.66, 95% CI 0.61-0.71, p < 0.001, I² = 0%; PPS: HR = 0.70, 95% CI 0.57–0.88, p < 0.01, I² = 70.2%). However, such a survival benefit was not observed when reoperation was considered as a time-dependent covariate (OS: HR = 2.19, 95% CI 1.47–3.27, p < 0.001; PPS: HR = 0.95, 95% CI 0.82–1.10, p = 0.51, I² = 0%). The estimate bias caused by ignoring the time-dependent nature of reoperation was further demonstrated by the re-analysis of survival data in three included studies.

Conclusions: The timing of reoperation may have an impact on the survival outcome in recurrent GBM patients, and survival benefits of reoperation in recurrent GBM may be overestimated when analyzed as fixed covariates. Proper analysis methodology should be used in future work to confirm the clinical benefits of reoperation.

Prognostic factors for survival in adult patients with recurrent glioblastoma: a decision-tree-based model

We assessed prognostic factors in relation to OS from progression in recurrent glioblastomas. Retrospective multicentric study enrolling 407 (training set) and 370 (external validation set) adult patients with a recurrent supratentorial glioblastoma treated by surgical resection and standard combined chemoradiotherapy as first-line treatment. Four complementary multivariate prognostic models were evaluated: Cox proportional hazards regression modeling, single-tree recursive partitioning, random survival forest, conditional random forest. Median overall survival from progression was 7.6 months (mean, 10.1; range, 0-86) and 8.0 months (mean, 8.5; range, 0-56) in the training and validation sets, respectively (p = 0.900). Using the Cox model in the training set, independent predictors of poorer overall survival from progression included increasing age at histopathological diagnosis (aHR, 1.47; 95% CI [1.03-2.08]; p = 0.032), RTOG-RPA V-VI classes (aHR, 1.38; 95% CI [1.11-1.73]; p = 0.004), decreasing KPS at progression (aHR, 3.46; 95% CI [2.10-5.72]; p < 0.001), while independent predictors of longer overall survival from progression included surgical resection (aHR, 0.57; 95% CI [0.44-0.73]; p < 0.001) and chemotherapy (aHR, 0.41; 95% CI [0.31-0.55]; p < 0.001). Single-tree recursive partitioning identified KPS at progression, surgical resection at progression, chemotherapy at progression, and RTOG-RPA class at histopathological diagnosis, as main survival predictors in the training set, yielding four risk categories highly predictive of overall survival from progression both in training (p < 0.0001) and validation (p < 0.0001) sets. Both random forest approaches identified KPS at progression as the most important survival predictor. Age, KPS at progression, RTOG-RPA classes, surgical resection at progression and chemotherapy at progression are prognostic for survival in recurrent glioblastomas and should inform the treatment decisions.