ArticleQuantitative imaging in medicine and surgery2024
White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07482423 (A Phase I/II Randomized, Double-Blind, Placebo-Controlled Trial to Evaluate the Safety and Potential Efficacy of XVIE Injected Intradermally in Patients With Androgenetic Alopecia), which is not on this map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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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.
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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.
A Phase I/II Randomized, Double-Blind, Placebo-Controlled Trial to Evaluate the Safety and Potential Efficacy of XVIE Injected Intradermally in Patients With Androgenetic Alopecia
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Enhancing Parkinson's Disease Staging: An Integrative Deep Learning Framework for Multimodal Feature Selection.Journal of molecular neuroscience : MN · 2026Article
- Utilizing combined quantitative multiparametric MRI as potential biomarkers for improved early-stage parkinson's disease diagnosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Multimodal diagnostic tools and advanced data models for detection of prodromal Parkinson's disease: a scoping review.BMC medical imaging · 2025Article
- Cross-modal fusion of brain imaging and clinical data for Parkinson's disease progression prediction.PloS one · 2025Article
- Personalized progression modelling and prediction in Parkinson's disease with a novel multi-modal graph approach.NPJ Parkinson's disease · 2024Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health. Methods: This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 Results: In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively. Conclusions: The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.
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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 Socratic graph.