Project ID: P202103100004
Robust identification of Parkinson’s disease subtypes using radiomics and hybrid machine learning
People with the below expertise are able to apply for this project: The individuals with enough experience in machine learning algorithms including dimension reduction algorithms, classifiers and clustering algorithms. The individual with enough experience in extracting radiomics features from each region of interest (via SERA package). The individual with enough experience in segmenting different regions via Free Surfer.
Objectives: It is important to subdivide Parkinson’s disease (PD) into subtypes, enabling potentially earlier disease recognition and tailored treatment strategies. We aimed to identify reproducible PD subtypes robust to variations in the number of patients and features. Methods: We applied multiple feature reduction and cluster-analysis methods to cross-sectional and timeless data, extracted from longitudinal datasets (years 0, 1, 2 & 4; Parkinson’s Progressive Marker Initiative; 885 PD/163 healthy-control visits; 35 datasets with combinations of non-imaging, conventional-imaging, and radiomics features from DAT-SPECT images). Hybrid machine-learning systems were constructed invoking 16 featurereduction algorithms, 8 clustering algorithms, and 16 classifiers (C-index clustering evaluation used on each trajectory). We subsequently performed: i) identification of optimal subtypes, ii) multiple independent tests to assess reproducibility, iii) further confirmation by a statistical approach, iv) test of reproducibility to the size of the samples.