ArticleMovement disorders : official journal of the Movement Disorder Society2026
A Brainstem Radiomics Framework to Distinguish Progressive Supranuclear Palsy from Parkinson's Disease.
Article in Movement disorders : official journal of the Movement Disorder Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundDifferentiating 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.
objectiveTo assess the diagnostic value of brainstem radiomic features from T1-weighted magnetic resonance imaging (MRI) in distinguishing PSP from PD patients.
methodsThis 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.
resultsRadiomics-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.
conclusionsBrainstem 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.