TY - JOUR
T1 - Survival prediction of glioblastoma patients—are we there yet?
T2 - A systematic review of prognostic modeling for glioblastoma and its clinical potential
AU - Tewarie, Ishaan Ashwini
AU - Senders, Joeky T.
AU - Kremer, Stijn
AU - Devi, Sharmila
AU - Gormley, William B.
AU - Arnaout, Omar
AU - Smith, Timothy R.
AU - Broekman, Marike L.D.
N1 - Funding
Open access funding provided by Leiden University Medical Center (LUMC). This study did not receive funding from internal or external sources.
Publisher Copyright:
© 2020, The Author(s).
PY - 2021
Y1 - 2021
N2 - Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58–0.98), accuracy (0.69–0.98), and C-index (0.66–0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion.
AB - Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58–0.98), accuracy (0.69–0.98), and C-index (0.66–0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion.
UR - http://www.scopus.com/inward/record.url?scp=85095699463&partnerID=8YFLogxK
U2 - 10.1007/s10143-020-01430-z
DO - 10.1007/s10143-020-01430-z
M3 - Review article
C2 - 33156423
AN - SCOPUS:85095699463
SN - 0344-5607
VL - 44
SP - 2047
EP - 2057
JO - Neurosurgical Review
JF - Neurosurgical Review
IS - 4
ER -