Can Ultrasound With Contrast Enhancement Replace Nonenhanced Computed Tomography Scans in Patients With Contraindication to Computed Tomography Contrast Agents?
Bibliographic record
Abstract
PURPOSE: Our purpose is to determine the efficacy of ultrasound (US), with the addition of contrast enhancement (CEUS), in the identification and characterization of abdominal pathology compared with nonenhanced computed tomography (CT) scan (NECT). METHODS: This prospective cohort study recruited 197 patients with NECT, the majority with renal failure, to have US, with addition of CEUS, if focal pathology was detected, occurring in 145 patients. Nonenhanced CT scan, US, and CEUS images/video files were presented to 2 blinded readers, in anonymous order. Examination quality and positive observations were recorded. True diagnosis was determined with pathology, follow-up imaging, and clinical notes. Data analysis showed sensitivity of NECT and US in the identification and characterization of pathology and sensitivity of CEUS to characterize abnormalities. RESULTS: Most pathology involved liver (n = 87), kidney (n = 35), and peritoneum (n = 13). Ultrasound alone was superior to NECT in the identification of hepatic and renal pathology, with both performing poorly at characterization. With addition of CEUS, characterization of hepatic and renal pathology reached 100%. Nonenhanced CT is superior to US in identification of peritoneal pathology, especially in large patients. Further solid and hollow organ pathology identified and characterized was of insufficient size to draw conclusions. CONCLUSIONS: Nonenhanced CT has limited ability to identify and characterize solid and hollow organ pathology. Ultrasound with the benefit of CEUS is superior to NECT in the characterization of focal liver, kidney, and peritoneal pathology. Contrast-enhanced ultrasound outperforms NECT in evaluation of suspect abdominal pathology in those with renal failure.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".