An Algorithm for the Diagnosis of Focal Liver Masses Using Microbubble Contrast-Enhanced Pulse-Inversion Sonography
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to develop an algorithm for liver mass diagnosis using microbubble contrast-enhanced pulse-inversion sonography. SUBJECTS AND METHODS: Ninety-six lesions in 92 patients were evaluated with DMP 115 (Definity)-enhanced pulse-inversion sonography, comprising 44 malignancies (29 hepatocellular carcinomas, 12 metastases, two peripheral cholangiocarcinomas, and one hepatic lymphoma) and 52 benign lesions (26 hemangiomas, 20 focal nodular hyperplasias, and six others). All had continuous low-mechanical-index imaging through the arterial and portal venous phase. A three-person blind review evaluated single images at baseline, early and peak arterial phases, and through the extended portal phases with a movie showing arterial phase wash-in. Reviewers assessed lesional vascularity and enhancement blindly but did not make a diagnosis. Combinations of answers were compared with independently determined final diagnoses to develop an algorithm for liver mass diagnosis. RESULTS: Portal phase enhancement comprises the first step of the algorithm, with positive or sustained enhancement identifying 48 (92%) of 52 benign lesions and negative enhancement or washout present in 41 (93%) of 44 malignancies. Sustained portal phase enhancement with arterial phase peripheral nodularity and centripetal progression predicted 24 (92%) of 26 of the hemangiomas; diffuse arterial phase enhancement greater than the liver identified 19 (95%) of 20 of the focal nodular hyperplasias. With negative portal phase enhancement, arterial phase information was less effective at differentiating hepatocellular carcinoma (25 [86%] of 29 cases) from another hepatic malignancy (11 [73%] of 15 cases). CONCLUSION: A simple diagnostic algorithm for interpretation of microbubble-enhanced sonography provides sensitive and accurate diagnosis of commonly encountered liver masses.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".