Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine

Abstract

Purpose: Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transformative technology in this field for patients. To illustrate, we describe AI-based advances in the diagnosis and management of gliomas, the most common primary central nervous system (CNS) malignancy. Methods: Presented is a succinct description of foundational concepts of AI approaches and their relevance to clinical medicine, geared toward clinicians without computer science backgrounds. We also review novel AI approaches in the diagnosis and management of glioma. Results: Novel AI approaches in gliomas have been developed to predict the grading and genomics from imaging, automate the diagnosis from histopathology, and provide insight into prognosis. Conclusion: Novel AI approaches offer acceptable performance in gliomas. Further investigation is necessary to improve the methodology and determine the full clinical utility of these novel approaches.

Multifractal Properties of BK Channel Currents in Human Glioblastoma Cells

“…The AI methods can be used for diagnosing, managing, and designing drugs against gliomas. In the literature, there already exist some reports like ref ( 55 ), where the authors present novel AI approaches to predict the grading and genomics from imaging, automate the diagnosis from histopathology, and provide insight into prognosis. Taking into account the therapeutic potential of gBK channel modulators in the treatment of glioblastoma, one could propose some AI methods to determine a group of active substances that could act as a drug against gliomas.…”

MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting

“…Recent improvements in ML algorithms and computational power provide an attractive venue for exploring MR radiomic features, an excellent fit for ML-approach analysis that considers the large data size and multimodal nature. Therefore, ML methods have been recently explored to predict glioma genetic biomarkers from MRI radiomic features [10][11][12][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

“…In recent years, utilizing machine learning (ML) methods for characterizing gliomas from medical imaging have attracted attention [11]. With regards to predicting glioma characteristics from MRI radiomic features, studies have primarily explored support vector machines (SVM) and random forest (RF) classifiers [11,12]. Recently, a new open source highly scalable gradient tree boosting model named eXtreme Gradient Boosting (XGBoost) has been introduced with some promising results [13].…”

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

“…Artificial intelligence, also known as machine learning, is a nonlinear mathematical modeling technology that is used extensively in modern day living, such as email communications, social media, web searching, stores and services, banking and finance, aviation and prediction of machinery failure, maps and directions, criminology, and war. 15,31 Recently, artificial intelligence is being increasingly used in clinical medicine including gastroenterology 32,33 endoscopy, 34 and hepatology, 15 radiology, 35 pathology, 36 dentistry, 37 oncology, 38 cardiology, 39 dermatology, 40 neurosurgery, 41 gynecology, 42 and in medical research, particularly big data analysis. Whereas convolutional neural network is the usual network used for image analysis, 43 feed-forward multilayer perceptron networks are the modeling technique for clinical prediction and have been used in the current study as well.…”