Helical CT with CT Angiography in Assessing Periampullary Neoplasms: Identification of Vascular Invasion
Bibliographic record
Abstract
PURPOSE: To determine the accuracy of helical computed tomography (CT) with CT angiography in identifying vascular invasion by periampullary neoplasms and to assess the added value of CT angiography. MATERIALS AND METHODS: Sixty-nine patients suspected of having periampullary neoplasms were examined. Images from dual phase helical CT with CT angiography were compared with surgical findings in 36 patients. Arterial and venous invasion were assessed separately. Accuracy, positive predictive value (PPV), and negative predictive value (NPV) were determined for CT alone and for CT supplemented with CT angiography. RESULTS: The accuracy, PPV, and NPV of helical CT with CT angiography in identifying venous invasion was 92% (33 of 36 patients), 86% (12 of 14 patients), and 95% (21 of 22 patients), respectively. When transverse CT images alone were analyzed, accuracy decreased to 69% (25 of 36 patients) (P =.005); PPV and NPV were 63% (five of eight patients) and 71% (20 of 28 patients), respectively. When identifying arterial invasion, the accuracy of CT with CT angiography and of CT alone was 86% (31 of 36 patients). PPV and NPV also were identical at 71% (five of seven patients) and 90% (26 of 29 patients), respectively. CONCLUSION: CT angiography significantly increases the ability to identify venous invasion when compared with CT alone but does not improve detection of arterial invasion.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".