A Continuous Correlation Between Residual Tumor Volume and Survival Recommends Maximal Safe Resection in Glioblastoma Patients: A Nomogram for Clinical Decision Making and Reference for Non-Randomized Trials.

Marco Skardelly,Jürgen Hedderich,Marlene Kaltenstadler,Felix Behling,Jens Gempt,Marcos Soares Tatagiba,Ghazaleh Tabatabai,Irina Mäurer,Mirjam Renovanz,Bernhard Meyer,Melanie Barz,Benjamin Bender,Frank Paulsen,Jens Schittenhelm

doi:10.3389/fonc.2021.748691

Abstract

ObjectiveThe exact role of the extent of resection or residual tumor volume on overall survival in glioblastoma patients is still controversial. Our aim was to create a statistical model showing the association between resection extent/residual tumor volume and overall survival and to provide a nomogram that can assess the survival benefit of individual patients and serve as a reference for non-randomized studies.MethodsIn this retrospective multicenter cohort study, we used the non-parametric Cox regression and the parametric log-logistic accelerated failure time model in patients with glioblastoma. On 303 patients (training set), we developed a model to evaluate the effect of the extent of resection/residual tumor volume on overall survival and created a score to estimate individual overall survival. The stability of the model was validated by 20-fold cross-validation and predictive accuracy by an external cohort of 253 patients (validation set).ResultsWe found a continuous relationship between extent of resection or residual tumor volume and overall survival. Our final accelerated failure time model (pseudo R2 = 0.423; C-index = 0.749) included residual tumor volume, age, O6-methylguanine-DNA-methyltransferase methylation, therapy modality, resectability, and ventricular wall infiltration as independent predictors of overall survival. Based on these factors, we developed a nomogram for assessing the survival of individual patients that showed a median absolute predictive error of 2.78 (mean: 1.83) months, an improvement of about 40% compared with the most promising established models.ConclusionsA continuous relationship between residual tumor volume and overall survival supports the concept of maximum safe resection. Due to the low absolute predictive error and the consideration of uneven distributions of covariates, this model is suitable for clinical decision making and helps to evaluate the results of non-randomized studies.

Highlights

Glioblastoma (GBM) is a prognostically unfavorable primary brain tumor with an incidence rate of 3.2 per 100,000 population, representing 14.5% of all primary brain tumors [1]
Out of 392 isocitrate dehydrogenase (IDH) wild-type GBM patients who were treated in our hospitals between 2006 and 2014, 303 patients had complete data sets and were available as a training set for multivariable regressions
We evaluated the effects of extent of resection (EOR) on survival using nonparametric and parametric survival models, demonstrated the advantages and limitations of the accelerated failure time (AFT) model, and provided an improved nomogram-based prediction model

Summary

Introduction

Glioblastoma (GBM) is a prognostically unfavorable primary brain tumor with an incidence rate of 3.2 per 100,000 population, representing 14.5% of all primary brain tumors [1]. Marko et al proposed a continuous relationship of EOR and survival times, showing that any degree of tumor resection is beneficial, and concluded that a maximum safe resection is generally indicated [4]. Marko et al were the first group to present data based on a parametric model of survival analysis, the accelerated failure time (AFT) model, instead of the commonly used semiparametric proportional hazard models. They suggested that their model had better explanatory capacity for survival prediction than other published models

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Conclusion