Determination of antibodies in autoimmune encephalitis diseases using machine learning

Zielsetzung Our aim is to predict the antibody status of patients with autoimmune encephalitis in a fully automated, non-invasive way using machine learning. For this purpose, radiomics-based machine learning models are developed on the basis of the patient's MRI images.

Material und Methoden Our study cohort consists of 98 patients with autoimmune encephalitis and known antibody status. Antibodies were previously detected in 57 of the 98 patients, and no antibodies were detected in the remaining 41 patients. We extracted 107 radiomic factors from the corresponding MRI images and tested a total of 6 different machine learning algorithms. Specifically, we used the Random forest algorithm, Naive Bayes, linear discriminant analysis (LDA), lasso regression, ridge regression and a neural network to predict antibody status. Each model was developed 100 times with new training data and subsequently tested each time with new independent test data in order to accurately assess the stability of the model results.

Ergebnisse Our results show that antibody status in patients with autoimmune encephalitis can be determined with high accuracy using machine learning algorithms based on MRI images. We obtained our best results with a lasso regression. Using independent test data, our 6-feature model yielded a mean AUC of 95.0%, a mean accuracy of 89.2%, a mean sensitivity of 89.2% and a mean specificity of 89.1%. Further algorithms, such as the neural network we tested, also resulted in high discriminatory power. Thus, our machine learning models show very high and stable performance in predicting antibody status in autoimmune encephalitis diseases, i.e. in distinguishing patients with detectable and undetectable antibodies.

Schlussfolgerungen Antibody status in patients with autoimmune encephalitis can be determined non-invasively and fully automatically with high accuracy using machine learning algorithms.

Publikationsverlauf

Artikel online veröffentlicht:
12. April 2024