18f-Fluorodeoxyglucose Positron-Emission Tomography for the Investigation of Malignancy in Patients with Suspected Paraneoplastic Neurologic Syndromes and Negative or Indeterminate Conventional Imaging: A Retrospective Analysis of the Ontario Pet Access Program, with Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: Paraneoplastic neurologic syndrome (pns) is a rare condition indirectly caused by an underlying malignancy. In many cases, the malignancy is occult at the time of the pns diagnosis, and the optimal diagnostic modality to detect the underlying tumour is unclear. In the present study, we aimed to assess the utility of 18F-fluorodeoxyglucose positron-emission tomography (fdg-pet) or pet integrated with computed tomography (pet/ct) in the investigation of these patients. Methods: We retrospectively analyzed data from the PET Access Program (pap) database in the province of Ontario to identify patients who underwent fdg-pet/ct imaging as part of a workup for pns. In all patients, prior conventional imaging was negative or indeterminate. To determine the diagnostic accuracy of fdg-pet/ct, data about demographics, presenting symptoms, and biochemical and radiologic workup, including fdg-pet/ct imaging results, were compared with data collected by the Ontario Cancer Registry (ocr). A systematic review of the literature and meta-analysis using our study inclusion criteria were performed for studies of fdg-pet accuracy. Results: Of 29 patients identified in the pap database, 9 had fdg-pet/ct results suspicious for malignancy. When correlated with data from the ocr, 5 fdg-pet/ct results were informative, resulting in a detection rate of 17%. Local sensitivity and specificity were 0.83 and 0.83 respectively. Two studies meeting our criteria were identified in the literature. The pooled sensitivity and specificity from the literature and local data were 0.88 and 0.90 respectively. Conclusions: When investigating for underlying malignancy in patients with suspected pns and negative conventional imaging, pet has high sensitivity and specificity.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".