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Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential.

ABSTRACT: 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.

SUBMITTER: Tewarie IA

PROVIDER: S-EPMC8338817 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The nutritional status of a patient has prognostic potency concerning short- and long-term outcomes, including survival, in many diseases. The controlling nutritional status (CONUT) score is a method for assessing nutritional status and predicting outcomes of several diseases. This study sought to systematically identify the prognostic role of preoperative CONUT score on posttreatment overall survival (OS), recurrence-free survival (RFS), and cancer-specific survival (CSS) in patients with cancer. The PubMed, SCOPUS, and Google Scholar databases and Google were searched for all dates until December 2019. Original articles investigating the association of preoperative CONUT score with survival in cancer patients who underwent surgery were included. Duplicate and irrelevant reports were screened out and the remaining articles assessed for quality and data extracted during critical analysis. Results of multivariate analysis were used to evaluate the prognostic competence of CONUT score in predicting survival. The search method identified an initial 181 articles, of which 32 were included in the final analysis. Lower OS, CSS, and RFS rates were reported by 100%, 100%, and 87.0% of the included studies, respectively, in cancer patients with high CONUT scores. A prognostic role of the CONUT score for prediction of OS, CSS, and RFS in cancer patients was shown by 91.7%, 90.9%, and 52.6% of the studies, respectively. The receiver operating characteristic curve area under the curve (AUC) value of the CONUT score for predicting OS, CSS, and RFS was at an acceptable level (>0.5) in all studies with available AUC values (n = 19). Sixty percent (12 of 20) of the studies reported that high CONUT score was significantly related to lower BMI. The findings promote confidence that a high preoperative CONUT score is associated with poor survival rate and is an independent prognostic factor of OS and CSS in patients with various types of cancer. Evaluation of the preoperative CONUT score might help clinicians in decision-making with respect to surgical implications.

Project description:PurposeConstruction of radiomics models for the individualized estimation of multiple survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI that could facilitate clinical decision-making for GBM patients.Materials and methodsA total of 134 eligible GBM patients were selected from The Cancer Genome Atlas. These patients were separated into the long-term and short-term survival groups according to the median of individual survival indicators: overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). Then, the patients were divided into a training set and a validation set in a ratio of 2:1. Radiomics features (n = 5,152) were extracted from multiple regions of the GBM using multiparametric MRI. Then, radiomics signatures that are related to the three survival indicators were respectively constructed using the analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) regression for each patient in the training set. Based on a Cox proportional hazards model, the radiomics model was further constructed by combining the signature and clinical risk factors.ResultsThe constructed radiomics model showed a promising discrimination ability to differentiate in the training set and validation set of GBM patients with survival indicators of OS, PFS, and DSS. Both the four MRI modalities and five tumor subregions have different effects on the three survival indicators of GBM. The favorable calibration and decision curve analysis indicated the clinical decision value of the radiomics model. The performance of models of the three survival indicators was different but excellent; the best model achieved C indexes of 0.725, 0.677, and 0.724, respectively, in the validation set.ConclusionOur results show that the proposed radiomics models have favorable predictive accuracy on three survival indicators and can provide individualized probabilities of survival stratification for GBM patients by using multiparametric and multiregional MRI features.

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Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential.

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