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.

Keywords: artificial intelligence; convolution neural network; deep neural network; glioma; neural network; support vector machines.

Figure 1

The relationship between the most common AI methods in medicine. SMV, Support Vector Machine; RF, Random forest algorithm; GBM, Gradient Boosting Machines; XGB, XGBoost.

Figure 2

A hyperplane separating two classes of data points in 2D space (A, blue line). A hyperplane separating two classes of data points in 3D space (B, blue sheet). Separation in more dimensions is also performed but it is difficult to be presented on a 2D manuscript. The support vectors are data that are closer to the hyperplane. The larger the margin between the hyperplanes, the better the classification (C,D).

Figure 3

Random forest algorithms by developing multiple decision trees and merging them get more accurate predictions.

Figure 4

Artificial neural network with a single hidden layer. There is complete connection between layers. Blue circles: Input layer. Red circles: Hidden layer. Yellow circle: Output layer.

Figure 5

Deep feedforward neural network with two hidden layers. Blue circles: Input layer. Red circles: Hidden layer. Yellow circle: Output layer.

Figure 6

A Convolution Neural Network (CNN) by multiple pooling and convolution steps before a deep neural network is now the most common AI algorithm for image analysis.