Role of SUVmax obtained by 18F-FDG PET/CT in patients with a solitary pancreatic lesion
Bibliographic record
Abstract
OBJECTIVES: Maximum standardized uptake value (SUV(max)) is a marker of tumor glucose metabolism detected by [(18)F]-fluorodeoxyglucose ((18)F-FDG) PET/computed tomography (PET/CT) and reflects tumor aggressiveness. The aim of the study was to evaluate the value of SUV(max) in differentiating benign from malignant solitary pancreatic lesions and explore the correlation between SUV(max) and tumor proliferative activity. MATERIALS AND METHODS: F-FDG PET/CT scans were performed in 80 patients with solitary pancreatic lesions who were scheduled for resective surgery. The relationships between SUV(max) and postoperative pathologic diagnosis, histologic grade, and Ki-67 proliferation index (PI) were analyzed. RESULTS: Of these 80 patients, 54 had malignant lesions. The SUV(max) of malignant tumors (6.3 ± 2.4) was significantly greater than that of benign lesions (2.9 ± 2.0) (P<0.001). Receiver-operating characteristic curve analysis showed that the SUV(max) cutoff value of 3.5 had a high sensitivity (92.6%) and specificity (76.9%) for the diagnosis of malignancies. Among pancreatic cancers with low (Ki-67<5%), moderate (5% ≤ Ki-67<50%), and high (Ki-67 ≥ 50%) PI, SUV(max) increased significantly from 4.2 ± 1.2, through 6.0 ± 1.7, to 8.6 ± 2.5 (P<0.001). The SUV(max) had a positive correlation with Ki-67 PI (P<0.001, r=0.60). CONCLUSION: The SUV(max) of F-FDG PET/CT can be used in the differential diagnosis of solitary pancreatic lesions and can also aid in the prediction of proliferative activity of pancreatic cancer.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".