Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of low-grade gliomas using imaging features based on the updated classification. We introduce molecular (MGMT methylation, IDH mutation, 1p/19q co-deletion, ATRX mutation, and TERT mutations) prediction methods of low-grade gliomas with imaging. Imaging features are extracted from magnetic resonance imaging data and include texture features, fractal and multi-resolution fractal texture features, and volumetric features. Training models include nested leave-one-out crossvalidation to select features, train the model, and estimate model performance. The prediction models of MGMT methylation, IDH mutations, 1p/19q co-deletion, ATRX mutation, and TERT mutations achieve a test performance AUC of 0.83 ± 0.04, 0.84 ± 0.03, 0.80 ± 0.04, 0.70 ± 0.09, and 0.82 ± 0.04, respectively. Furthermore, our analysis shows that the fractal features have a significant effect on the predictive performance of MGMT methylation IDH mutations, 1p/19q co-deletion, and ATRX mutations. The performance of our prediction methods indicates the potential of correlating computed imaging features with LGG molecular mutations types and identifies candidates that may be considered potential predictive biomarkers of LGG molecular classification. Diffuse low-grade gliomas (LGG) are World Health Organization (WHO) Grade II and III gliomas. They are infiltrative in their nature and arising from glial cells (astrocytes or oligodendrocytes) of the central nervous system (CNS) 1,2. Recurrence and malicious progression are possible because of the difficulty in complete tumor resection 3. A group of these tumors may also develop into glioblastoma (GBM). An updated classification of diffuse LGG was included in the 2016 WHO Classification of Tumors of the CNS 4. The new classification of the diffuse LGG depends on the genetic driver mutations (IDH mutations, 1p/19q co-deletion, TERT mutations, and ATRX mutations). This new classification correlates well with patients' treatment and survival, for example, oligodendroglioma, defined by the 1p/19q co-deletion, are associated with longer survival compared to astrocytoma, which do not harbor the 1p/19q co-deletion 5. Molecular mutations are determined using invasive methods by obtaining usable tissue samples that have an increase in proliferation and neovascularization 6. Tissue sampling may also be associated with high cost, morbidity, and even mortality 7 , and depending on the sample, may undersample tumor components, especially in heterogeneous tumors. Consequently, developing alternative methods and non-invasively classify diffuse LGG into its different subtypes using imaging features and machine learning techniques have emerged as a promising body of research. In this work, we propose a non-invasive imaging-based...

Increasing clinical demands have coincided with destructive effects in the work environment and the ability and desire of neuroradiologists in the United States to perform academic or practice-building duties with a substantial incidence of burnout symptoms. While this survey does not prove causation, the trends and the correlations should be concerning to the leaders of radiology and warrant further monitoring.