Differential diagnosis of small hepatocellular nodules in cirrhosis: surrogate histological criteria of <i>TERT</i> promoter mutations
Bibliographic record
Abstract
AIMS: The differential diagnosis of small hepatocellular nodules in cirrhosis between dysplastic nodules and hepatocellular carcinoma (HCC) remains challenging on biopsy. As TERT promoter (pTERT) mutations may indicate the nodules already engaged in the malignant process, the aim of this study was to identify histological criteria associated with pTERT mutations by detecting these mutations by ddPCR in small formalin-fixed paraffin-embedded (FFPE) hepatocellular nodules arising in cirrhosis. METHODS AND RESULTS: We built a bicentric cohort data set of 339 hepatocellular nodules < 2 cm from cirrhotic samples, divided into a test cohort of 299 resected samples and a validation cohort of 40 biopsies. Pathological review, based on the evaluation of 14 histological criteria, classified all nodules. pTERT mutations were identified by ddPCR in FFPE samples. Among the 339 nodules, ddPCR revealed pTERT mutations in 105 cases (31%), including 90 and 15 cases in the test and validation cohorts, respectively. On multivariate analysis, three histological criteria were associated with pTERT mutations in the test cohort: increased cell density (P = 0.003), stromal invasion (P = 0.036) and plate-thickening anomalies (P < 0.001). With the combination of at least two of these major criteria, the AUC for predicting pTERT mutations was 0.84 in the test cohort (sensitivity: 86%, specificity: 83%) and 0.81 in the validation cohort (sensitivity: 87%, specificity: 76%). CONCLUSIONS: We identified three histological criteria as surrogate markers of pTERT mutations that may be used in routine biopsy to more clearly classify small hepatocellular nodules arising in cirrhosis.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".