Occult liver nodules: their detection and characterization with CEUS
Bibliographic record
Abstract
Abstract Objectives Contrast enhanced ultrasound (CEUS) now joins the ranks of CT and MRI for noninvasive diagnosis of hepatocellular carcinoma (HCC). CEUS LI-RADS provides greater than 95% specificity for diagnosis within LR-5. Unlike CT/MRI, CEUS is nodule based. Currently, LI-RADS does not recommend CEUS of nodules occult or invisible on pre-contrast ultrasound except by experts. This study addresses our ability to find occult nodules using CEUS and characterize them with CEUS LI-RADS. Methods 100 patients at risk for HCC, 81 with cirrhosis, with occult lesions were retrospectively identified from our archived patient logs. All patients had CEUS examination. Three specialized CEUS techniques (blindshot injection, portal venous (PVP) sweep of the liver, and on-top injection) are used to evaluate nodules. Results There were 114 occult lesions in 100 patients. The origin of 78(68%) lesions was an MRI (n = 69) or CT scan (n = 9) with an observation of abnormal enhancement, generally arterial phase hyperenhancement (APHE). All these patients had blindshot CEUS injection looking for a correlate with APHE. The remainder of occult lesions (n = 36)(32%) were first detected during CEUS, generally as washout foci on PVP sweeps or incidental APHE or washout nearby other targets. All washout areas had subsequent on-top injection to assess for APHE. Application of CEUS LI-RADS algorithm categorized 26 LR-5, 34 LR-4, and 5 LR-M. CEUS upgraded LI-RADS category of 24/50(48%) occult lesions reported on CT/MRI. 29(25%) occult lesions were offered treatment and from categories LR-5 and LR-M, 5 had biopsy confirmation and 15 were treated. From both sources, MR/CT and CEUS, there were 12 occult lesions scanned for treatment response, categorized as 7 LR-TR viable, 1 LR-TR nonviable, and 4 LR-TR equivocal on CEUS. Conclusion Our study shows we can find and characterize occult nodules using CEUS techniques and CEUS LI-RADS algorithm, with positive impact on clinical management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".