Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning
Fellows’ Journal Club
The authors evaluated the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma (n=35) and enhancing glioma (n=71). The mean areas under the receiver operating characteristic curve were 0.877 for the support vector machine classifier; 0.878 for reader 1; 0.899 for reader 2; and 0.845 for reader 3. They conclude that support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma.