Characteristics and prognostic factors of age-stratified high-grade intracranial glioma patients: A population-based analysis

A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas

Lower-grade gliomas (LGG) can eventually progress to glioblastoma (GBM) and death. In the context of the transfer learning approach, we aimed to train and test an MRI-based radiomics model for predicting survival in GBM patients and validate it in LGG patients. From each patient's 704 MRI-based radiomics features, we chose seventeen optimal radiomics signatures in the GBM training set (n = 71) and used these features in both the GBM testing set (n = 31) and LGG validation set (n = 107) for further analysis. Each patient's risk score, calculated based on those optimal radiomics signatures, was chosen to represent the radiomics model. We compared the radiomics model with clinical, gene status models, and combined model integrating radiomics, clinical, and gene status in predicting survival. The average iAUCs of combined models in training, testing, and validation sets were respectively 0.804, 0.878, and 0.802, and those of radiomics models were 0.798, 0.867, and 0.717. The average iAUCs of gene status and clinical models ranged from 0.522 to 0.735 in all three sets. The radiomics model trained in GBM patients can effectively predict the overall survival of GBM and LGG patients, and the combined model improved this ability.

ABCB1 Is Frequently Methylated in Higher-Grade Gliomas and May Serve as a Diagnostic Biomarker of More Aggressive Tumors

ABCB1 belongs to a superfamily of membrane transporters that use ATP hydrolysis to efflux various endogenous compounds and drugs outside the cell. Cancer cells upregulate ABCB1 expression as an adaptive response to evade chemotherapy-mediated cell death. On the other hand, several reports highlight the role of the epigenetic regulation of ABCB1 expression. In fact, the promoter methylation of ABCB1 was found to be methylated in several tumor types, including gliomas, but its role as a biomarker is not fully established yet. Thus, the aim of this study was to analyze the methylation of the ABCB1 promoter in tumor tissues from 50 glioma patients to verify its incidence and to semi-quantitively detect ABCB1 methylation levels in order to establish its utility as a potential biomarker. The results of this study show a high interindividual variability in the ABCB1 methylation level of the samples derived from gliomas of different grades. Additionally, a positive correlation between ABCB1 methylation, the WHO tumor grade, and an IDH1 wild-type status has been observed. Thus, ABCB1 methylation can be regarded as a potential diagnostic or prognostic biomarker for glioma patients, indicating more aggressive tumors.

Practice of the new supervised machine learning predictive analytics for glioma patient survival after tumor resection: Experiences in a high-volume Chinese center

OBJECTIVE

This study aims to assess the effectiveness of the Gradient Boosting (GB) algorithm on glioma prognosis prediction and to explore new predictive models for glioma patient survival after tumor resection.

METHODS

A cohort of 776 glioma cases (WHO grades II-IV) between 2010 and 2017 was obtained. Clinical characteristics and biomarker information were reviewed. Subsequently, we constructed the conventional Cox survival model and three different supervised machine learning models, including support vector machine (SVM), random survival forest (RSF), Tree GB, and Component GB. Then, the model performance was compared with each other. At last, we also assessed the feature importance of models.

RESULTS

The concordance indexes of the conventional survival model, SVM, RSF, Tree GB, and Component GB were 0.755, 0.787, 0.830, 0.837, and 0.840, respectively. All areas under the cumulative receiver operating characteristic curve of both GB models were above 0.800 at different survival times. Their calibration curves showed good calibration of survival prediction. Meanwhile, the analysis of feature importance revealed Karnofsky performance status, age, tumor subtype, extent of resection, and so on as crucial predictive factors.

CONCLUSION

Gradient Boosting models performed better in predicting glioma patient survival after tumor resection than other models.

GLIMPSE: a glioblastoma prognostication model using ensemble learning-a surveillance, epidemiology, and end results study

Purpose

Glioblastoma is one of the most common and aggressive brain tumors in the world with a poor prognosis. A glioblastoma prognostication model has the potential to improve the cancer's standard of care. No other paper has looked at using ensemble learning with a population database to predict multiple binary glioblastoma survival outcomes.

Methods

We utilized ensemble learning to design, build, and test a prognostication system for glioblastoma for short-, intermediate- and long-term survival, based on various clinical features. We used the population database SEER which covers 17 different registries. The most important prognostic features were identified and used as a clinical feature set. The statistical feature set was determined using Random Forests. The accuracy, sensitivity, specificity, area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were reported.

Results

Statistically-determined feature sets had the best performance. All the top models for short, intermediate, and long-term survival were random forests. With regards to short-term survival, top model had metrics AUROC = 0.937, accuracy = 86%, specificity = 88%, sensitivity = 85%, NPV = 85%, and PPV = 87%. For long-term survival, the top model had AUROC = 0.893, accuracy = 81%, specificity = 79%, sensitivity = 83%, NPV = 82%, and PPV = 79%. The top intermediate-term survival prediction had AUROC ≥ 0.780 and the other metrics were at least 70%.

Conclusions

Our ensemble models were high-performing and achieved AUROCs as high as 0.94, highlighting the importance of balancing, using ensemble techniques and statistical feature selection. Our models can potentially be used by clinicians after external validation.