Differentiating between common PSP phenotypes using structural MRI: a machine learning study

• Published in: Journal of neurology Vol. 270; no. 11; pp. 5502 - 5515
• Main Authors: Quattrone, Andrea, Sarica, Alessia, Buonocore, Jolanda, Morelli, Maurizio, Bianco, Maria Giovanna, Calomino, Camilla, Aracri, Federica, De Maria, Marida, Vescio, Basilio, Vaccaro, Maria Grazia, Quattrone, Aldo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Algorithms; Basal ganglia; Brain diseases; Central nervous system diseases; Classification; Clinical trials; Differential diagnosis; Learning algorithms; Machine learning; Magnetic resonance imaging; Medicine; Medicine & Public Health; Movement disorders; Neurology; Neuroradiology; Neurosciences; Original Communication; Phenotypes; Progressive supranuclear palsy; Ventricle; Ventricles (cerebral); Progressive supranuclear palsy-Richardson’s syndrome; MRPI; Cortical thickness; Progressive supranuclear palsy-Parkinsonism; Machine learning
• ISSN: 0340-5354; 1432-1459; 1432-1459
• DOI: 10.1007/s00415-023-11892-y

Background Differentiating Progressive supranuclear palsy-Richardson’s syndrome (PSP-RS) from PSP-Parkinsonism (PSP-P) may be extremely challenging. In this study, we aimed to distinguish these two PSP phenotypes using MRI structural data. Methods Sixty-two PSP-RS, 40 PSP-P patients and 33 control subjects were enrolled. All patients underwent brain 3 T-MRI; cortical thickness and cortical/subcortical volumes were extracted using Freesurfer on T1-weighted images. We calculated the automated MR Parkinsonism Index (MRPI) and its second version including also the third ventricle width (MRPI 2.0) and tested their classification performance. We also employed a Machine learning (ML) classification approach using two decision tree-based algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) with different combinations of structural MRI data in differentiating between PSP phenotypes. Results MRPI and MRPI 2.0 had AUC of 0.88 and 0.81, respectively, in differentiating PSP-RS from PSP-P. ML models demonstrated that the combination of MRPI and volumetric/thickness data was more powerful than each feature alone. The two ML algorithms showed comparable results, and the best ML model in differentiating between PSP phenotypes used XGBoost with a combination of MRPI, cortical thickness and subcortical volumes (AUC 0.93 ± 0.04). Similar performance (AUC 0.93 ± 0.06) was also obtained in a sub-cohort of 59 early PSP patients. Conclusion The combined use of MRPI and volumetric/thickness data was more accurate than each MRI feature alone in differentiating between PSP-RS and PSP-P. Our study supports the use of structural MRI to improve the early differential diagnosis between common PSP phenotypes, which may be relevant for prognostic implications and patient inclusion in clinical trials.