Features of fibrosis regression abound in “non-cirrhotic” patients with resected hepatocellular carcinoma
Bibliographic record
Abstract
Cirrhosis is a major risk factor for developing hepatocellular carcinoma (HCC). However, many surgically resected HCCs are presumably non-cirrhotic. The dynamic nature of chronic liver disease leads to periods of hepatic repair and fibrosis regression. We hypothesize that most resected HCCs, including those from non-cirrhotic patients, exhibit features of fibrosis regression in their background liver, suggesting previously more advanced liver disease. We reviewed the histology of 37 HCC resections performed between 2005-2020, including 30 from non-cirrhotic patients. The non-neoplastic liver was evaluated for features of liver disease and of the hepatic repair complex (HRC). CD34 immunohistochemistry was performed as a marker of sinusoidal capillarization. CD34 staining was evaluated manually and also by a digital image classifier algorithm. Overall, 28 cases (76%) had a high number of fibrosis regression and hepatic repair features (≥4 out of 8 features). Amongst the 30 non-cirrhotic patients, 21 (70%) showed a high number of repair features. Relative CD34 expression was increased in cases with a high number (≥4) of HRC features versus a low number (≤3) of features (p = 0.019). High HRC cases were more likely to exhibit nodular circumferential CD34 staining (p = 0.019). Our findings suggest that most resected HCC from non-cirrhotic patients display features of fibrosis regression in their background liver. Thus many, if not most, HCC patients who are "non-cirrhotic" may in fact have regressed cirrhosis. This finding reinforces that patients with regressed cirrhosis continue to be at high risk for 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".