Nodules Identified on Surveillance Ultrasound for <scp>HCC</scp>
Bibliographic record
Abstract
OBJECTIVES: Following positive surveillance ultrasound (US), magnetic resonance imaging (MRI) is recommended for further characterization. We propose contrast-enhanced ultrasound (CEUS) shows equivalent efficacy. METHODS: This prospective institutional review board approved study recruited 195 consecutive at-risk patients with a positive surveillance US. All had CEUS and MRI. Biopsy (n = 44) and follow-up are gold standard. MRI and CEUS results are classified according to liver imaging reporting and data system (LI-RADS) and patient outcome. RESULTS: As an US-based modality, CEUS is superior in confirming findings from surveillance US, correlation in 189/195 (97%) on CEUS compared to 153/195 (79%) on MRI. Within these negative MRI examinations, there are 2 hepatocellular carcinoma (HCC) and 1 cholangiocarcinoma (iCCA) diagnosed on CEUS and proven by biopsy. From 195 patients, there are 71 malignant diagnoses from all sources, including 58 LR-5 (45 on MRI and 54 on CEUS) and 13 others, including HCC outside of LR-5 category, and LR-M with biopsy proven iCCA (3 on MRI and 6 on CEUS). CEUS and MRI show concordant results in the majority of patients (146/195, 75%), including 57/146 malignant and 89/146 benign diagnoses. There are 41/57 concordant LR-5 and 6/57 concordant LR-M. When CEUS and MRI are discordant, CEUS upgraded 20 (10 biopsy-proven) from MRI LR-3/4 to CEUS LR-5 or LR-M by showing washout (WO) that MRI failed to show. Additionally, CEUS characterized time and intensity of WO and diagnosed 13/20 LR-5 by showing late and weak WO and 7 LR-M by showing fast and marked WO. CEUS is 81% sensitive and 92% specific in diagnosing malignancy. MRI is 64% sensitive and 93% specific. CONCLUSIONS: CEUS performance is at least equivalent if not superior to MRI for initial evaluation of lesions from surveillance US.
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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 | 0.002 |
| 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) | 0.002 | 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".