A potential biomarker for treatment stratification in psychosis: evaluation of an [(18)F] FDOPA PET imaging approach

[(18)F]FDOPA PET imaging has shown dopaminergic function indexed as K(i)(cer) differs between antipsychotic treatment responders and non-responders. However, the theragnostic potential of this biomarker to identify non-responders has yet to be evaluated. In view of this, we aimed to evaluate this as a theragnostic test using linear and non-linear machine-learning (i.e., Bernoulli, support vector, random forest and Gaussian processes) analyses and to develop and evaluate a simplified approach, standardised uptake value ratio (SUVRc). Both [(18)F]FDOPA PET approaches had good test-rest reproducibility across striatal regions (K(i)(cer) ICC: 0.68–0.94, SUVRc ICC: 0.76–0.91). Both our linear and non-linear classification models showed good predictive power to distinguish responders from non-responders (receiver operating curve area under the curve for region-of-interest approach: K(i)(cer) = 0.80, SUVRc = 0.79; for voxel-wise approach using a linear support vector machine: 0.88) and similar sensitivity for identifying treatment non-responders with 100% specificity (K(i)(cer): ~50%, SUVRc: 40–60%). Although the findings were replicated in two independent datasets, given the total sample size (n = 84) and single setting, they warrant testing in other samples and settings. Preliminary economic analysis of [(18)F]FDOPA PET to fast-track treatment-resistant patients with schizophrenia to clozapine indicated a potential healthcare cost saving of ~£3400 (equivalent to $4232 USD) per patient. These findings indicate [(18)F]FDOPA PET dopamine imaging has potential as biomarker to guide treatment choice.