Indeterminate 1-2-cm nodules found on hepatocellular carcinoma surveillance: Biopsy for all, some, or none?
Bibliographic record
Abstract
UNLABELLED: In the latest hepatocellular carcinoma (HCC) management guidelines by the American Association for the Study of Liver Diseases, biopsy is advocated for all nodules deemed indeterminate after imaging work-up by contrast-enhanced scans. However, the latest guidelines' imaging work-up algorithm has been shown to improve sensitivity of characterization of HCC for 1-2-cm nodules, decreasing the proportion of HCCs that remain indeterminate after imaging work-up. We undertook a study of 1-2-cm indeterminate nodules to determine what proportions are malignant and which variables can be used to limit biopsy to a subset of nodules at higher risk of malignancy. Eighty consecutive patients with 93 indeterminate nodules were included. Final diagnosis was established in 85 nodules, with 13 malignant (9 by biopsy, 4 by growth) and 72 benign (stability of ≥18 months). Cause of liver disease, ethnicity, size, arterial hypervascularity, venous hypoenhancement, and presence of synchronous typical HCC were analyzed by univariate logistic analysis to determine significant predictors of malignancy. Rate of malignancy among indeterminate 1-2-cm nodules was found to be 14%-23%. Only arterial hypervascularity [odds ratio (OR), 3.7) and presence of synchronous HCC (OR, 7.1) were significant predictors of malignancy. A strategy of limiting biopsy to nodules that had either feature would result in 23 biopsies and potentially detect 8 of 13 malignant nodules, yielding a sensitivity of 62% and specificity of 79%. CONCLUSION: The prevalence of malignancy among 1-2-cm indeterminate nodules is low (14%-23%), and biopsy of all such nodules results in many negative results. Limiting biopsy to nodules with arterial hypervascularity or in the presence of a synchronous typical HCC would detect the majority of HCCs while substantially reducing the number of biopsies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".