Accuracy of Cytology vs. Microbiopsy for the Diagnosis of Well-Differentiated Hepatocellular Carcinoma and Macroregenerative Nodule
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy of ultrasound (US)-guided fine needle aspiration (FNA) for the diagnosis of well-differentiated hepatocellular carcinoma (wd HCC) and macroregenerative nodule (MRN) and to identify the most useful cytologic and histologic criteria to distinguish between those two diagnoses. STUDY DESIGN: Cytologic and histologic specimens of 50 wd HCC and 50 MRN were reviewed blindly and the diagnosis compared to the final clinical diagnosis. Twenty-eight cytologic and 25 histologic criteria were examined and subjected to statistical analysis. RESULTS: Among 100 cases studied, the final diagnosis was available for 43. In those 43 cases, combining analysis of cytologic and histologic specimens, the sensitivity of US-guided FNA was of 75% and the specificity 100%. Cytologic analysis was better than isolated histologic analysis, with a sensitivity of 75% vs. 68%, respectively. Sensitivity of cytologic diagnosis was lower for smaller nodules and for those located in poorly accessible hepatic segments. With the use of stepwise logistic regression analysis, four cytologic features (increased nuclear/cytoplasmic ratio, cellular monomorphism, nuclear crowding, loss of bile duct cells) and four histologic features (increased nuclear/cytoplasmic ratio, decreased Kupffer cells, cellular monomorphism, increased trabeculae thickness) were identified as predictive of HCC.
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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 | 0.003 | 0.021 |
| 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 | 0.001 |
| 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".