The influence of time interval between diagnostic image acquisition and operative date on pathologic tumor size in pancreatic adenocarcinoma: implications for local therapy
Bibliographic record
Abstract
Objectives: Computed tomography (CT) and magnetic resonance imaging (MRI) may underestimate pancreatic tumor size, which has important implications for local therapy. Our aim was to determine if tumor growth during the interval between image acquisition and operative date impacted the observed size discrepancy. Methods: Tumor sizes measured on preoperative MRI were compared with gross pathological specimen measurements in 148 patients with surgically resected pancreatic adenocarcinoma. Differences in the measurements were correlated with the interval between date of pre-operative MRI acquisition and date of operation. Differences between tumor size on MRI and pathology reports were compared with respect to the intervening time interval. Results: A total of 148 patients had pre-operative MRI scans and were included in the analysis. The median patient age was 66 years (range: 29 years-86 years). A significant under estimation of 4.5 mm between tumor size measured on preoperative MRI and pathological examination ( p < .001) was demonstrated. There was no significant correlation between size discrepancy and time interval from the diagnostic imaging study and the surgical procedure (R 2 = 0.001, p = .72). Conclusions: Time interval between the acquired diagnostic imaging study and operative date appears to have no measureable influence on radiographic to pathologic size discrepancy in pancreatic adenocarcinoma. MRI was again shown to underestimate pancreatic cancer tumor size. Additional exploration into the role of MRI in delineating pancreatic tumor volume with a prospectively designed study is needed to validate these findings.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".