Radiomic features of amygdala nuclei and hippocampus subfields help to predict subthalamic deep brain stimulation motor outcomes for Parkinson's disease patients

Ausra Saudargiene; Andrius Radziunas; Justinas J Dainauskas; Vytautas Kucinskas; Paulina Vaitkiene; Aiste Pranckeviciene; Ovidijus Laucius; Arimantas Tamasauskas; Vytenis Deltuva

doi:10.3389/fnins.2022.1028996

Radiomic features of amygdala nuclei and hippocampus subfields help to predict subthalamic deep brain stimulation motor outcomes for Parkinson's disease patients

Front Neurosci. 2022 Oct 13:16:1028996. doi: 10.3389/fnins.2022.1028996. eCollection 2022.

Authors

Affiliations

¹ Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania.
² Department of Neurosurgery, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania.
³ Department of Health Psychology, Faculty of Public Health, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania.
⁴ Department of Neurology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania.

Abstract

Background and purpose: The aim of the study is to predict the subthalamic nucleus (STN) deep brain stimulation (DBS) outcomes for Parkinson's disease (PD) patients using the radiomic features extracted from pre-operative magnetic resonance images (MRI).

Methods: The study included 34 PD patients who underwent DBS implantation in the STN. Five patients (15%) showed poor DBS motor outcome. All together 9 amygdalar nuclei and 12 hippocampus subfields were segmented using Freesurfer 7.0 pipeline from pre-operative MRI images. Furthermore, PyRadiomics platform was used to extract 120 radiomic features for each nuclei and subfield resulting in 5,040 features. Minimum Redundancy Maximum Relevance (mRMR) feature selection method was employed to reduce the number of features to 20, and 8 machine learning methods (regularized binary logistic regression (LR), decision tree classifier (DT), linear discriminant analysis (LDA), naive Bayes classifier (NB), kernel support vector machine (SVM), deep feed-forward neural network (DNN), one-class support vector machine (OC-SVM), feed-forward neural network-based autoencoder for anomaly detection (DNN-A)) were applied to build the models for poor vs. good and very good STN-DBS motor outcome prediction.

Results: The highest mean prediction accuracy was obtained using regularized LR (96.65 ± 7.24%, AUC 0.98 ± 0.06) and DNN (87.25 ± 14.80%, AUC 0.87 ± 0.18).

Conclusion: The results show the potential power of the radiomic features extracted from hippocampus and amygdala MRI in the prediction of STN-DBS motor outcomes for PD patients.

Keywords: Parkinson’s disease; amygdala; deep brain stimulation; hippocampus; motor outcome prediction; radiomic features.