A Novel Comprehensive Clinical Stratification Model to Refine Prognosis of Glioblastoma Patients Undergoing Surgical Resection

Abstract

Despite recent discoveries in genetics and molecular fields, glioblastoma (GBM) prognosis still remains unfavorable with less than 10% of patients alive 5 years after diagnosis. Numerous studies have focused on the research of biological biomarkers to stratify GBM patients. We addressed this issue in our study by using clinical/molecular and image data, which is generally available to Neurosurgical Departments in order to create a prognostic score that can be useful to stratify GBM patients undergoing surgical resection. By using the random forest approach [CART analysis (classification and regression tree)] on Survival time data of 465 cases, we developed a new prediction score resulting in 10 groups based on extent of resection (EOR), age, tumor volumetric features, intraoperative protocols and tumor molecular classes. The resulting tree was trimmed according to similarities in the relative hazard ratios amongst groups, giving rise to a 5-group classification tree. These 5 groups were different in terms of overall survival (OS) (p < 0.000). The score performance in predicting death was defined by a Harrell’s c-index of 0.79 (95% confidence interval [0.76–0.81]). The proposed score could be useful in a clinical setting to refine the prognosis of GBM patients after surgery and prior to postoperative treatment.

Characteristics of Fluorescent Intraoperative Dyes Helpful in Gross Total Resection of High-Grade Gliomas—A Systematic Review

“…For this reason, it is incredibly important to achieve the most extensive resection feasible. It has been repeatedly demonstrated that the extent of resection (EOR) is directly related to the outcome of treatment of patients with gliomas and is an important predictive factor [ 33 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 ]. For glioblastoma, Stummer et al showed the effect of EOR on 6 months of PFS (progression-free survival) in the range of 21.1 to 41% [ 82 ].…”

Is age an additional factor in the treatment of elderly patients with glioblastoma? A new stratification model: an Italian Multicenter Study

“…The methods of this study were based on a previous study in which a scoring system for patients of all ages with GBM was elaborated. 21…”

“…To determine subgroup patients with different clinical prognoses, we used the decision tree model with the classification and regression tree (CART) method. 21,26 This method is a machine learning model composed of hierarchical decision rules involving optimal cutoff values that recursively split independent factors into different groups. The groups of individuals are called nodes and form a branch node tree.…”

“…With the aim of evaluating the role of tumor growth pattern on overall survival (OS), the preoperative MRI index was assessed as follows: T1/T2 = preoperative volumetric tumor volume on postcontrast T1-weighted images/ preoperative volumetric tumor volume on T2-weighted images. 21,25…”

“…17,18 An increasing number of prognostic survival tools are being developed to combine clinical, radiological, and molecular variables in an all-inclusive risk stratification model. [20][21][22] Given the importance of each individual factor, it is often difficult to establish how these interact with each other and how they impact prognosis in the complexity of clinical settings. Cox survival analysis generally detects risk factors without highlighting how their interactions or various combinations influence the prognosis.…”