Radiomic Analysis of Striatal [18F]FDOPA PET Imaging in Patients with Psychosis for the Identification of Antipsychotic Response

Mol Imaging Biol. 2025 May 5. doi: 10.1007/s11307-025-02014-3. Online ahead of print.

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.

PMID:40323469 | DOI:10.1007/s11307-025-02014-3