Radiogenomic Predictors of Recurrence in Glioblastoma—A Systematic Review

Abstract

Glioblastoma, as the most aggressive brain tumor, is associated with a poor prognosis and outcome. To optimize prognosis and clinical therapy decisions, there is an urgent need to stratify patients with increased risk for recurrent tumors and low therapeutic success to optimize individual treatment. Radiogenomics establishes a link between radiological and pathological information. This review provides a state-of-the-art picture illustrating the latest developments in the use of radiogenomic markers regarding prognosis and their potential for monitoring recurrence. Databases PubMed, Google Scholar, and Cochrane Library were searched. Inclusion criteria were defined as diagnosis of glioblastoma with histopathological and radiological follow-up. Out of 321 reviewed articles, 43 articles met these inclusion criteria. Included studies were analyzed for the frequency of radiological and molecular tumor markers whereby radiogenomic associations were analyzed. Six main associations were described: radiogenomic prognosis, MGMT status, IDH, EGFR status, molecular subgroups, and tumor location. Prospective studies analyzing prognostic features of glioblastoma together with radiological features are lacking. By reviewing the progress in the development of radiogenomic markers, we provide insights into the potential efficacy of such an approach for clinical routine use eventually enabling early identification of glioblastoma recurrence and therefore supporting a further personalized monitoring and treatment strategy.

Association between the Anatomical Location of Glioblastoma and Its Evaluation with Clinical Considerations: A Systematic Review and Meta-Analysis

“…Again, our review differs due to the characteristics previously mentioned. Lastly, the review by Corr [ 135 ] showed as its main results that there has been a lack of prospective studies analyzing the prognostic characteristics of glioblastoma together with its anatomical and radiological characteristics, which would eventually facilitate the early identification of glioblastoma recurrence, thus supporting a more personalized treatment and follow-up strategy. Although that study does not make the case specifically, it supports the findings of the current review and highlights the importance of specific knowledge and location of the GBM which can lead to better management and treatment of patients with this pathology.…”

XGBoost algorithm and logistic regression to predict the postoperative 5-year outcome in patients with glioma

“…Some previous studies outlined the predictors of glioma prognosis (16)(17)(18), including patient's history of diseases, such as the results of preoperative laboratory test results, the patient's living habits, co-existing diseases, and other often-used information (including age and gender, etc. ); preoperative information of imaging examination, including the location and volume of tumor, and its relationship with adjacent tissues; and results of pathological examination, such as pathological type.…”

Disease-Based Prognostication: Neuro-Oncology

“…Advanced MRI, computational techniques, and radiogenomics offer promise in our ability to diagnose and prognosticate glioblastoma more accurately. 34 35 Diffusion-weighted imaging may be used to assess high cellularity in glioma, differentiating low-grade tumors from glioblastoma, and to assess the tumor microenvironment (TME). MR perfusion can also provide this information while also assessing treatment effects and characterizing pseudoprogression and pseudoresponse.…”