Radiomic Analysis of Striatal [18F]FDOPA PET Imaging in Patients with Psychosis for the Identification of Antipsychotic Response
Year: 2025
Type: article
Study design: Observational Study
Abstract: Purpose Schizophrenia (SCZ) is a severe psychiatric disorder marked by abnormal dopamine synthesis, measurable through [18F]FDOPA PET imaging. This imaging technique has been proposed as a biomarker for treatment stratification in SCZ, where one-third of patients respond poorly to standard antipsychotics. This study explores the use of radiomics on [18F]FDOPA PET data to examine dopamine synthesis in SCZ and predict antipsychotic response. Methods We analysed 273 [18F]FDOPA PET scans from healthy controls (n = 138) and SCZ patients (n = 135) from multiple cohorts, including first-episode psychosis cases. Radiomic features from striatal regions were extracted using the MIRP Python package. Reproducibility was assessed with test–retest scans, selecting features with an intraclass correlation coefficient (ICC) > 0.80. These features were grouped via hierarchical clustering based on Spearman correlation. Regression analysis evaluated sex and age effects on radiomic features. Predictive power for treatment response was tested and compared to standard imaging analysis obtained from the Standardised Uptake Value ratio (SUVr) of striatal over cerebellar tracer activity. Results Out of 177 features, 15 met the ICC criteria (ICC: 0.81–0.99). Age and sex influenced features in patients but not in controls. The best performance were was by the GLCM joint maximum feature, which effectively differentiated responders from non-responders (AUC: 0.66–0.87), but did not reach statistical significance in classification over SUVr. Conclusion Radiomic analysis of [18F]FDOPA PET supports its use as a biomarker for assessing antipsychotic efficacy in schizophrenia, highlighting differential striatal tracer uptake based on patient response. While it provides modest classification improvements over standard imaging, further validation in larger datasets and integration with multivariate classification algorithms are needed.
Source: Molecular Imaging and Biology
Authors: Astrid Schiulaz, Giovanna Nordio, Alessio Giacomel, Rubaida Easmin, Andrea Bettinelli, Pierluigi Selvaggi, Steven Williams, Federico Turkheimer, Sameer Jauhar, Oliver Howes, Mattia Veronese, FDOPA PET Imaging Working Group, Ilinca Angelescu, Micheal Bloomfield, Ilaria Bonoldi, Faith Borgan, Tarik Dahoun, Enrico D’Ambrosio, Arsime Demjaha, Jecek Donocik, Alice Egerton, Stephen Kaar, Euitae Kim, Seoyoung Kim, James Maccabe, Julian Matthews, Robert McCutcheon, Philip McGuire, Chiara Nosarti, Matthew Nour, Maria Rogdaki, Grazia Rutigliano, Peter S. Talbot, Luke Vano
Institutions: University of Padua, King's College London, Istituto Oncologico Veneto, University of Bari Aldo Moro, Hammersmith Hospital, MRC London Institute of Medical Sciences, Imperial College London, Medical Research Council, South London and Maudsley NHS Foundation Trust, University College London, Seoul National University Bundang Hospital, University of Manchester, University of Oxford
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,190
OpenAlex ID: W4410091549