Detection of antibodies in suspected autoimmune encephalitis diseases using machine learning

• Published in: Scientific reports Vol. 15; no. 1; pp. 10998 - 11
• Main Authors: Musigmann, Manfred, Spiekers, Christine, Stake, Jacob, Akkurt, Burak Han, Mora, Nabila Gala Nacul, Sartoretti, Thomas, Heindel, Walter, Mannil, Manoj
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 31.03.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/4028; 692/53; 692/617; 692/699; Adolescent; Adult; Aged; Algorithms; Autoantibodies; Autoantibodies - blood; Autoantibodies - immunology; Autoimmune diseases; Autoimmune encephalitis; Diagnosis; Encephalitis; Encephalitis - blood; Encephalitis - diagnosis; Encephalitis - diagnostic imaging; Encephalitis - immunology; Female; Hashimoto Disease - blood; Hashimoto Disease - diagnosis; Hashimoto Disease - diagnostic imaging; Hashimoto Disease - immunology; Hippocampus; Humanities and Social Sciences; Humans; Laboratory tests; Learning algorithms; Machine Learning; Magnetic Resonance Imaging - methods; Male; Middle Aged; MRI; multidisciplinary; Neural networks; Neural Networks, Computer; Open source software; Radiomics; Science; Science (multidisciplinary); Seropositivity; Young Adult; Neuroimaging; Seropositivity; MRI; Machine learning; Hippocampus; Autoimmune encephalitis; Radiomics
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-95815-z

In our study, we aim to predict the antibody serostatus of patients with suspected autoimmune encephalitis (AE) using machine learning based on pre-contrast T2-weighted MR images acquired at symptom onset. A confirmation of seropositivity is of great importance for a reliable diagnosis in suspected AE cases. The cohort used in our study comprises 98 patients diagnosed with AE. 57 of these patients had previously tested positive for autoantibodies associated with AE. In contrast, no antibodies were detected in the remaining 41 patients. A manual bilateral segmentation of the hippocampus was performed using the open-source software 3D Slicer on T2-weighted MR-images. Subsequently, 107 Radiomics features were extracted from each T2-weighted MR image utilizing the open source PyRadiomics software package. Our study cohort was randomly divided into training and independent test data. Five conventional machine learning algorithms and a neural network were tested regarding their ability to differentiate between seropositive and seronegative patients. All performance values were determined based on independent test data. Our final model includes six features and is based on a Lasso regression. Using independent test data, this model yields a mean AUC of 0.950, a mean accuracy of 0.892, a mean sensitivity of 0.892 and a mean specificity of 0.891 in predicting antibody serostatus in patients with suspected AE. Our results show that Radiomics-based machine learning is a very promising method for predicting serostatus of suspected AE patients and can thus help to confirm the diagnosis. In the future, such methods could facilitate and accelerate the diagnosis of AE even before the results of specific laboratory tests are available, allowing patients to benefit more quickly from a reliable treatment strategy.