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

Abstract

Background and purposeThe 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).MethodsThe 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.ResultsThe 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).ConclusionThe 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.

Personalizing Deep Brain Stimulation Therapy for Parkinson’s Disease With Whole-Brain MRI Radiomics and Machine Learning

“…A different group used as "ground truth" the 30% decrease preoperatively of the Parkinson's Disease Composite scale, (a scale that measures disease severity taking into consideration motor and nonmotor symptoms as well as treatment-related complications) while using radiomics of the amygdala and hippocampus to classify good versus bad responders. Their LR model achieved an average AUC and accuracy of 0.98 and 0.96, respectively [14]. A third group used the off-state UPDRSIII (motor symptoms assessed without any medication) with a 30% cut-off decrease with respect to the postoperative off-state UPDRSIII.…”