4.7 Article

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

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NEUROPSYCHOPHARMACOLOGY
卷 46, 期 6, 页码 1122-1132

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SPRINGERNATURE
DOI: 10.1038/s41386-020-00866-7

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资金

  1. Medical Research Council-UK [MC-A656-5QD30]
  2. Maudsley Charity [666]
  3. Brain and Behaviour Research Foundation
  4. Wellcome Trust [094849/Z/10/Z]
  5. National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust
  6. King's College London
  7. National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust
  8. Wellcome Trust Digital Award [215747/Z/19/Z]
  9. JMAS SIM Fellowship from the Royal College of Physicians, Edinburgh
  10. Angellini
  11. AstraZeneca
  12. Autifony
  13. Biogen
  14. Boehringer-Ingelheim
  15. Eli Lilly
  16. Heptares
  17. Invicro
  18. Jansenn
  19. Lundbeck
  20. Lyden-Delta
  21. Mylan
  22. Neurocrine
  23. Otsuka
  24. Sunovion
  25. Rand
  26. Recordati
  27. Roche
  28. Wellcome Trust [215747/Z/19/Z] Funding Source: Wellcome Trust

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The study evaluated the potential of [F-18]FDOPA PET imaging to identify non-responders to antipsychotic treatment using linear and non-linear machine learning analyses and found good test-retest reproducibility and predictive power in distinguishing responders from non-responders. The economic analysis indicated potential healthcare cost savings by using [F-18]FDOPA PET to fast-track treatment-resistant patients to clozapine. Overall, the findings suggest that [F-18]FDOPA PET dopamine imaging has the potential to serve as a biomarker to guide treatment choice for patients with schizophrenia.
[F-18]FDOPA PET imaging has shown dopaminergic function indexed asK(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 [F-18]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): similar to 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 [F-18]FDOPA PET to fast-track treatment-resistant patients with schizophrenia to clozapine indicated a potential healthcare cost saving of similar to 3400 pound (equivalent to $4232 USD) per patient. These findings indicate [F-18]FDOPA PET dopamine imaging has potential as biomarker to guide treatment choice.

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