Resolution of indeterminate MRI with CEUS in patients at high risk for hepatocellular carcinoma
Bibliographic record
Abstract
PURPOSE: To show the contribution of CEUS to characterization of indeterminate MRI observations in high-risk patients for hepatocellular carcinoma (HCC). METHODS: From July to December 2015, 42 consecutive patients referred to CEUS with indeterminate MRI scans comprise our study cohort. There are 50 indeterminate nodule-like observations and 10 arterial phase hyperenhancing foci, suggesting pseudolesions/arterio-portal shunts. MRI and CEUS lesions are classified according to their enhancement features in all phases and Liver Imaging and Reporting Data System (LI-RADS) in a blind read format. Clinical pathologic correlation and 24 months follow-up are performed. RESULTS: A majority, 37/50 (74%), of indeterminate nodule-like observations have arterial phase enhancement without washout on MRI. CEUS further characterizes enhancement and shows washout in 14/37 (38%). In total, CEUS diagnoses 16 malignant lesions in 14 patients including 14 HCC and 2 ICC. 12/16 (75%) malignant lesions are confirmed by biopsy or follow-up. Ultrasound identification of a nodule differentiates real nodules from pseudolesions. Of the ten suspected arterial-portal shunts on MRI, two show a real nodule on ultrasound, confirmed as an HCC and a regenerative nodule. 15/42 (36%) patients have LI-RADS escalated from LR-3 or 4 on MRI to LR-4 or 5 on CEUS. Overall, the sensitivity of CEUS is (13/16) 81.3% and specificity is (37/37) 100% for malignant diagnosis. CONCLUSION: Grayscale ultrasound detects true nodules. Dynamic CEUS detects and characterizes washout, correctly predicting HCC. CEUS is complimentary to MRI and can serve as a problem-solving tool when MRI is indeterminate.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".