EANM perspective on clinical PET and SPECT imaging in schizophrenia-spectrum disorders: a systematic review of longitudinal studies
Bibliographic record
Abstract
Purpose: There is a need for biomarkers in psychiatry to improve diagnosis, prognosis and management, and with confirmed value in follow-up care. Radionuclide imaging, given its molecular imaging characteristics, is well-positioned for translation to the clinic. This systematic review lays the groundwork for integrating PET and SPECT imaging in the clinical management of schizophrenia-spectrum disorders. Methods: Systematic search of PubMed, Embase, Web of Science and Cochrane library databases was conducted from the earliest date available until February 2024. The focus was on longitudinal studies evaluating PET or SPECT imaging in individuals with a schizophrenia-spectrum or another psychotic disorders. Quality assessment was done using the Newcastle-Ottawa Scale (NOS), NIH scale for before-after studies and Cochrane Risk of Bias tool version 2 (Cochrane RoB2). Studies were further categorised into three groups: preclinical and diagnosis, predicting disease course or personalising treatment. Results: Fifty-six studies were included in the systematic review investigating in total 1329 patients over a median of 3 months. Over two-thirds used PET tracers, whereas the remaining studies employed SPECT tracers. The most frequently investigated system was dopaminergic transmission, followed by cerebral metabolism and blood flow. [18F]FDOPA demonstrated large effect size in predicting conversion of subjects at risk and treatment response. Additionally, treatment dosage could be optimised to reduce side effects using [123I]IBZM or [11C]raclopride. Conclusion: Molecular imaging holds significant promise for real-life application in schizophrenia, with two particularly encouraging avenues being the prediction of conversion/response to antipsychotic medication and the improved management of antipsychotic dosage. Further longitudinal studies and clinical trials will be essential for validating both the clinical effectiveness and economic sustainability, as well as for exploring new applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".