A Brainstem Radiomics Framework to Distinguish Progressive Supranuclear Palsy from Parkinson's Disease.

Overview

1. Neuroscience Research Center Magna Graecia University Catanzaro Italy
2. Brain Health Imaging Centre Centre for Addiction and Mental Health (CAMH) Toronto Canada
3. Biotecnomed S.C.aR.L. Catanzaro Italy
4. Azienda Ospedaliero‐Universitaria Renato Dulbecco Catanzaro Italy
5. Institute of Radiology Azienda Ospedaliero‐Universitaria Renato Dulbecco Catanzaro Italy
6. Institute of Neurology, Department of Medical and Surgical Sciences Magna Graecia University Catanzaro Italy

Abstract

Background: Differentiating progressive supranuclear palsy (PSP) from Parkinson's disease (PD) can be clinically challenging. In the neuroimaging field, radiomics has emerged as a promising approach to capture subtle microstructural and textural image alterations, improving differential diagnoses.

Objective: To assess the diagnostic value of brainstem radiomic features from T1‐weighted magnetic resonance imaging (MRI) in distinguishing PSP from PD patients.

Methods: This study included 433 participants from two independent cohorts: an Italian training cohort (84 PSP and 177 PD) and an international validation cohort (68 PSP and 104 PD). Radiomic features including first‐order, shape, and texture descriptors were extracted with PyRadiomics from brainstem segmentations generated by the automated deep‐learning‐based AssemblyNet pipeline. Classification models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost) were trained using nested cross‐validation and tested on the independent cohort. Model interpretability was examined with SHapley Additive exPlanations.

Results: Radiomics‐based models yielded high and consistent performance in distinguishing PSP from PD, higher than brainstem volume. In the validation cohort, Random Forest and XGBoost achieved the best performance (area under the curve [AUC]: 0.93 and 0.94, respectively). Texture‐ and intensity‐based radiomic features emerged as the most informative predictors, while shape descriptors showed lower relevance in discrimination between PSP and PD.

Conclusions: Brainstem radiomics extracted from routine T1‐weighted MRI demonstrated excellent classification performance in distinguishing PSP from PD patients and generalized robustly across independent datasets. Texture‐based features captured microstructural disorganization not reflected by automated volumetry, underscoring the added value of radiomics for differential diagnosis in atypical parkinsonism and for integration in future multimodal biomarker frameworks. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data Availability Statement

Data from the Parkinson's Progression Markers Initiative (PPMI) and the 4‐Repeat Tauopathy Neuroimaging Initiative (4RTNI) are publicly available through their respective data access procedures. Due to ethical and privacy restrictions, individual‐level clinical and imaging data from the training cohort are not publicly available but can be accessed from the corresponding author upon reasonable request. The code used for data processing and analysis is available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 2, 28 September 2026

• Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 12 MeSH terms, 1 funder, 61 references, 1 RRID.

Cite

This paper

Camastra, C., Buonocore, J., Augimeri, A., Calomino, C., Bianco, M. G., Sarica, A., Arcuri, P. P., Quattrone, A., & Quattrone, A. (2026). A Brainstem Radiomics Framework to Distinguish Progressive Supranuclear Palsy from Parkinson's Disease. Movement disorders : official journal of the Movement Disorder Society, 41(8), 2133-2142. https://doi.org/10.1002/mds.70334

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