Two decades of SPECT/CT – the coming of age of a technology: An updated review of literature evidence
Bibliographic record
Abstract
PURPOSE: Single-photon emission computed tomography (SPECT) combined with computed tomography (CT) was introduced as a hybrid SPECT/CT imaging modality two decades ago. The main advantage of SPECT/CT is the increased specificity achieved through a more precise localization and characterization of functional findings. The improved diagnostic accuracy is also associated with greater diagnostic confidence and better inter-specialty communication. METHODS: This review presents a critical assessment of the relevant literature published so far on the role of SPECT/CT in a variety of clinical conditions. It also includes an update on the established evidence demonstrating both the advantages and limitations of this modality. CONCLUSIONS: For the majority of applications, SPECT/CT should be a routine imaging technique, fully integrated into the clinical decision-making process, including oncology, endocrinology, orthopaedics, paediatrics, and cardiology. Large-scale prospective studies are lacking, however, on the use of SPECT/CT in certain clinical domains such as neurology and lung disorders. The review also presents data on the complementary role of SPECT/CT with other imaging modalities and a comparative analysis, where available.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".