A radiomics approach to distinguish Progressive Supranuclear Palsy Richardson's syndrome from other phenotypes starting from MR images.
Authors
Affiliations (5)
Affiliations (5)
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy.
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, 84131 Salerno, Italy.
- Department of Engineering, University of Sannio, 82100 Benevento, Italy.
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy. Electronic address: [email protected].
Abstract
Progressive Supranuclear Palsy (PSP) is an uncommon neurodegenerative disorder with different clinical onset, including Richardson's syndrome (PSP-RS) and other variant phenotypes (vPSP). Recognising the clinical progression of different phenotypes would enhance the accuracy of detection and treatment of PSP. The study goal was to identify radiomic biomarkers for distinguishing PSP phenotypes extracted from T1-weighted magnetic resonance images (MRI). Forty PSP patients (20 PSP-RS and 20 vPSP) took part in the present work. Radiomic features were collected from 21 regions of interest (ROIs) mainly from frontal cortex, supratentorial white matter, basal nuclei, brainstem, cerebellum, 3rd and 4th ventricles. After features selection, three tree-based machine learning (ML) classifiers were implemented to classify PSP phenotypes. 10 out of 21 ROIs performed best about sensitivity, specificity, accuracy and area under the receiver operating characteristic curve (AUCROC). Particularly, features extracted from the pons region obtained the best accuracy (0.92) and AUCROC (0.83) values while by using the other 10 ROIs, evaluation metrics range from 0.67 to 0.83. Eight features of the Gray Level Dependence Matrix were recurrently extracted for the 10 ROIs. Furthermore, by combining these ROIs, the results exceeded 0.83 in phenotypes classification and the selected areas were brain stem, pons, occipital white matter, precentral gyrus and thalamus regions. Based on the achieved results, our proposed approach could represent a promising tool for distinguishing PSP-RS from vPSP.