Preliminary evaluation of 18F-FDG-PET/MRI for differentiation of serous from nonserous pancreatic cystic neoplasms: a pilot study
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to evaluate preliminary feasibility of 18F-FDG-PET/MRI in differentiation of pancreatic serous cystic neoplasms (SCNs) from non-SCNs. METHODS: From August 2017 to June 2019, 10 patients (3 men, 7 women; mean age, 63 years) previously diagnosed with pancreatic cystic neoplasm underwent simultaneous 18F-FDG-PET/MRI prospectively on an integrated 3-Tesla hybrid PET/MRI scanner. PET images were analyzed visually and semiquantitatively measuring standardized uptake values (SUV) including lesion SUVmax and SUVmean, lesion to pancreas and lesion to liver SUVmax and SUVmean ratio independent of MRI diagnosis. The reference standard for lesion diagnosis was by MRI features and interval follow-up. RESULTS: Visual assessment of PET images demonstrated uptake in 57% of SCNs. Lesion to liver SUVmax ratio of ≥0.5 showed the highest accuracy (90%) and area under the curve (0.9) followed by lesion SUVmax of ≥1.6 and lesion to pancreas SUVmax ratio of ≥0.77 for diagnosis of SCN. The sensitivity for lesion SUVmax of ≥1.6 was less than two other ones (71 versus 100%). All non-SCNs exhibited SUVmax value less than 1.6 while 33 and 66% demonstrated lesion to liver SUVmax ratio of >0.5 and lesion to pancreas SUVmax ratio of >0.77, respectively. PET/MRI specificity was 67, 100, 67 and 33% through lesion to liver SUVmax ratio, lesion SUVmax, lesion SUVmean and lesion to pancreas SUVmax ratio, respectively, for diagnosis of SCN. CONCLUSION: Preliminary results show that PET/MRI utilization is promising for differentiation of pancreatic SCN from non-SCN lesions. This could reduce need for surveillance imaging or avoidance of unnecessary intervention in pancreatic cystic neoplasms with uncertain diagnoses.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".