Contrast-enhanced ultrasound liver imaging reporting and data system: clinical validation in a prospective multinational study in North America and Europe
Bibliographic record
Abstract
BACKGROUND AND AIMS: The objective of this study is to determine the diagnostic accuracy of the American College of Radiology Contrast-Enhanced Ultrasound (CEUS) Liver Imaging Reporting and Data System LR-5 characterization for HCC diagnosis in North American or European patients. APPROACH AND RESULTS: A prospective multinational cohort study was performed from January 2018 through November 2022 at 11 academic and nonacademic centers in North America and Europe. Patients at risk for HCC with at least 1 liver observation not previously treated, identified on ultrasound (US), or multiphase CT or MRI performed as a part of standard clinical care were eligible for the study. All participants were examined with CEUS of the liver within 4 weeks of CT/MRI or tissue diagnosis to characterize up to 2 liver nodules per participant using ACR CEUS Liver Imaging Reporting and Data System. Definite HCC diagnosis on the initial CT/MRI, imaging follow-up, or histology for CT/MRI-indeterminate nodules were used as reference standards. A total of 545 nodules had confirmed reference standards in 480 patients, 73.8% were HCC, 5.5% were other malignancies, and 20.7% were nonmalignant. The specificity of CEUS LR-5 for HCC was 95.1% (95% CI 90.1%-97.7%), sensitivity 62.9% (95% CI 57.9%-67.7%), positive predictive value 97.3% (95% CI 94.5%-98.7%), and negative predictive value 47.7% (95% CI 41.7%-53.8%). In addition, benign CEUS characterization (LR-1 or LR-2) had 100% specificity and 100% positive predictive value for nonmalignant liver nodules. CONCLUSIONS: CEUS Liver Imaging Reporting and Data System provides an accurate categorization of liver nodules in participants at risk for HCC.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".