Multi–Detector Row Helical CT in Preoperative Assessment of Small (≤1.5 cm) Liver Metastases: Is Thinner Collimation Better?
Bibliographic record
Abstract
PURPOSE: To determine the value of collimations less than 5 mm in detecting hepatic metastases 1.5 cm or smaller by using multi-detector row helical computed tomography (CT). MATERIALS AND METHODS: Thirty-one patients underwent contrast material-enhanced multi-detector row helical CT before hepatic resection in this prospective study. Images were reconstructed at collimations of 5.00, 3.75, and 2.50 mm with 50% overlap and reviewed independently by three radiologists. Each lesion was characterized as metastatic, benign, or equivocal and graded for conspicuity. Criterion standards were pathologic assessment of the resected liver and follow-up of the nonresected liver. Only lesions 1.5 cm or smaller were analyzed. RESULTS: There were a total of 88 liver lesions 1.5 cm or smaller, and 25 of these were metastases. Pooled sensitivity for all lesions improved with thinner collimation (66% [58 of 88 lesions], 69% [61 of 88], and 82% [72 of 88] at collimations of 5.00, 3.75, and 2.50 mm, respectively), and this was statistically significant (P =.01). However, no significant difference was noted between collimations in the pooled sensitivity for metastatic lesions (80% [20 of 25 lesions] at all collimations) (P >.99). No statistical difference was noted in the conspicuity of lesions at different collimations (P =.18). CONCLUSION: Image reconstruction with multi-detector row helical CT at collimations less than 5 mm may not improve sensitivity in the detection of hepatic metastases 1.5 cm or smaller.
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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 | 0.013 |
| 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 | 0.001 |
| 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".