Negative Predictive Value of Contrast‐Enhanced Ultrasound in Differentiating Avascular Solid‐Appearing From Vascularized Masses: A Retrospective Consecutive Study
Bibliographic record
Abstract
OBJECTIVES: To determine the negative predictive value (NPV) of contrast-enhanced ultrasound (CEUS) to establish the lack of vascularity in a mass. METHODS: This work was an Institutional Review Board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study. Acquisition of consent was waived. We included all CEUS examinations performed for tissue characterization between 2004 and 2014 that reported showing no vascularity in a mass. Contrast-enhanced ultrasound findings were considered true-negative when there was stability on imaging for at least 1 year or no evidence of a solid mass, if biopsied, and false-negative if there was lesion growth on imaging within 12 months or an indication of a solid mass on the pathologic examination, if biopsied. One author reviewed all of the reports and follow-up examinations. We conducted a consensus review of all false-negative findings mixed with an equal number of true-negative findings by 2 reviewers, who were blinded to the final results. RESULTS: The study population consisted of 97 CEUS examinations in 97 patients, including 48 women and 49 men (mean age ± SD, 65 ± 14 years). Examinations were performed for lesion characterization in the liver (n = 23), pancreas (n = 17), kidney (n = 54), 1 gallbladder, 1 adnexa, and 1 peritoneal lesion. The overall false-negative rate on the official prospective review was 2% (2 of 97). Two false-negative findings were correctly identified on the consensus review. The NPV of CEUS was 97.9% (95 of 97; 95% confidence interval, 93%- 99%) on the official review. CONCLUSIONS: Contrast-enhanced ultrasound has a very high NPV to exclude the presence of flow in a mass, and it can be used to exclude the presence of a solid mass.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".