Machine Learning in Magnetic Resonance Images of Glioblastoma: A Review.

BACKGROUND: The purpose of this work was to identify which Glioblastoma (GBM) problems can be handled by Magnetic Resonance Imaging (MRI) and Machine Learning (ML) techniques. Results, limitations, and trends through a review of the scientific literature in the last 5 years were performed. Google Scholar, PubMed, Elsevier databases, and forward and backward citations were used for searching articles applying ML techniques in GBM. The 50 most relevant papers fulfilling the selection criteria were deeply analyzed. The PRISMA statement was followed to structure our report. METHODS: A partial taxonomy of the GBM problems tackled with ML methods was formulated with 15 subcategories grouped into four categories: extraction of characteristics from tumoral regions, differentiation, characterization, and problems based on genetics. RESULTS: The dominant techniques in solving these problems are: Radiomics for feature extraction, Least Absolute Shrinkage and Selection Operator for feature selection, Support Vector Machines and Random Forest for classification, and Convolutional Neural Networks for characterization. A noticeable trend is that the application of Deep Learning on GBM problems is growing exponentially. The main limitations of ML methods are their interpretability and generalization. CONCLUSION: The diagnosis, treatment, and characterization of GBM have advanced with the aid of ML methods and MRI data, and this improvement is expected to continue. ML methods are effective in solving GBM-related problems with different precisions, Overall Survival being the hardest problem to solve with accuracies ranging from 57%-71%, and GBM differentiation the one with the highest accuracy ranging from 80%-97%.

Auditory impairment in H-ABC tubulinopathy.

Hypomyelination with atrophy of the basal ganglia and cerebellum (H-ABC) is a neurodegenerative disease due to mutations in TUBB4A. Patients suffer from extrapyramidal movements, spasticity, ataxia, and cognitive deficits. Magnetic resonance imaging features are hypomyelination and atrophy of the striatum and cerebellum. A correlation between the mutations and their cellular, tissue and organic effects is largely missing. The effects of these mutations on sensory functions have not been described so far. We have previously reported a rat carrying a TUBB4A (A302T) mutation and sharing most of the clinical and radiological signs with H-ABC patients. Here, for the first time, we did a comparative study of the hearing function in an H-ABC patient and in this mutant model. By analyzing hearing function, we found that there are no significant differences in the auditory brainstem response (ABR) thresholds between mutant rats and WT controls. Nevertheless, ABRs show longer latencies in central waves (II-IV) that in some cases disappear when compared to WT. The patient also shows abnormal AEPs presenting only Waves I and II. Distortion product of otoacoustic emissions and immunohistochemistry in the rat show that the peripheral hearing function and morphology of the organ of Corti are normal. We conclude that the tubulin mutation severely impairs the central hearing pathway most probably by progressive central white matter degeneration. Hearing function might be affected in a significant fraction of patients with H-ABC; therefore, screening for auditory function should be done on patients with tubulinopathies to evaluate hearing support therapies.

Machine learning for condensed matter physics.

Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter at the quantum and atomistic levels, and describes how these interactions result in both mesoscopic and macroscopic properties. CMP overlaps with many other important branches of science, such as chemistry, materials science, statistical physics, and high-performance computing. With the advancements in modern machine learning (ML) technology, a keen interest in applying these algorithms to further CMP research has created a compelling new area of research at the intersection of both fields. In this review, we aim to explore the main areas within CMP, which have successfully applied ML techniques to further research, such as the description and use of ML schemes for potential energy surfaces, the characterization of topological phases of matter in lattice systems, the prediction of phase transitions in off-lattice and atomistic simulations, the interpretation of ML theories with physics-inspired frameworks and the enhancement of simulation methods with ML algorithms. We also discuss in detail the main challenges and drawbacks of using ML methods on CMP problems, as well as some perspectives for future developments.