Abstract

Background: Functional remodeling may vary with tumor aggressiveness of glioma. Investigation of the functional remodeling is expected to provide scientific relevance of tumor characterization and disease management of glioma. In this study, we aimed to investigate the functional remodeling of the contralesional hemisphere and its utility in predicting the malignant grade of glioma at the individual level with multivariate logistic regression (MLR) analysis. Subjects and Methods: One hundred and twenty-six right-handed subjects with histologically confirmed cerebral glioma were included with 80 tumors located in the left hemisphere (LH) and 46 tumors located in the right hemisphere (RH). Resting-state functional networks of the contralesional hemisphere were constructed using the human brainnetome atlas based on resting-state fMRI data. Functional connectivity and topological features of functional networks were quantified. The performance of functional features in predicting the glioma grade was evaluated using area under (AUC) the receiver operating characteristic curve (ROC). The dataset was divided into training and validation datasets. Features with high AUC values in malignancy classification in the training dataset were determined as predictive features. An MLR model was constructed based on predictive features and its classification performance was evaluated on the training and validation datasets with 10-fold cross validation. Results: Predictive functional features showed apparent hemispheric specifications. MLR classification models constructed with age and predictive functional connectivity features (AUC of 0.853 ± 0.079 and 1.000 ± 0.000 for LH and RH group, respectively) and topological features (AUC of 0.788 ± 0.150 and 0.897 ± 0.165 for LH and RH group, respectively) achieved efficient performance in predicting the malignant grade of gliomas. Conclusion: Functional remodeling of the contralesional hemisphere was hemisphere-specific and highly predictive of the malignant grade of glioma. Network approach provides a novel pathway that may innovate glioma characterization and management at the individual level.

Highlights

  • Cerebral glioma is the most frequently identified intracranial tumor in adults

  • We aimed to investigate the hemispheric specifications of glioma-induced functional remodeling and its relevance to tumor aggressiveness based on the functional connectivity and topological features of the resting-state functional networks, employing the structurally intact contralesional hemisphere as the alternative to the wholebrain maneuver

  • The multivariate logistic regression (MLR) model constructed with predictive functional connectivity (FC) features of left hemisphere (LH) group achieved the best performance of malignant grade estimation with Area under the curve (AUC) of 0.853 ± 0.079 on validation dataset (Table 2)

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Summary

Introduction

Cerebral glioma is the most frequently identified intracranial tumor in adults. As gliomas with different malignancy may progress in distinct proliferation kinetics (Tubiana, 1989; Louis et al, 2016), it is necessary to preoperatively estimate the biological aggressiveness of gliomas for the therapeutic formulation. With the burgeoning development of artificial intelligence and radiomics, a combination of high dimensional features derived from multimodal neuroimaging has achieved plausible accuracy in reflecting the biological aggressiveness of glioma (Tian et al, 2018; Yang et al, 2018) Disadvantages of these analysis paradigms still remain. We aimed to investigate the functional remodeling of the contralesional hemisphere and its utility in predicting the malignant grade of glioma at the individual level with multivariate logistic regression (MLR) analysis. MLR classification models constructed with age and predictive functional connectivity features (AUC of 0.853 ± 0.079 and 1.000 ± 0.000 for LH and RH group, respectively) and topological features (AUC of 0.788 ± 0.150 and 0.897 ± 0.165 for LH and RH group, respectively) achieved efficient performance in predicting the malignant grade of gliomas. Network approach provides a novel pathway that may innovate glioma characterization and management at the individual level

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