Hepatocellular carcinoma in patients cured of chronic hepatitis C: Minimal steatosis
Bibliographic record
Abstract
Abstract Background Successful treatment of hepatitis C reduces liver inflammation and fibrosis; however, patients remain at risk of developing hepatocellular carcinoma (HCC). Aims To identify risk factors for new‐onset HCC in patients cured of hepatitis C. Methods Imaging, histological, and clinical data on patients whose first HCC was diagnosed >12 months of post‐SVR were analyzed. Histology of 20 nontumor tissues was analyzed in a blinded manner using the Knodel/Ishak/HAI system for necroinflammation and fibrosis/cirrhosis stage and the Brunt system for steatosis/steatohepatitis. Factors associated with post‐SVR HCC were identified by comparison with HALT‐C participants who did not develop post‐SVR HCC. Results Hepatocellular carcinoma was diagnosed in 54 patients (45 M/9F), a median of 6 years of post‐SVR [interquartile range (IQR) =1.4‐10y] at a median age of 61 years (IQR, 59–67). Approximately one‐third lacked cirrhosis, and only 11% had steatosis on imaging. The majority (60%) had no steatosis/steatohepatitis in histopathology. The median HAI score was 3 (1.25–4), indicating mild necroinflammation. In a multivariable logistic regression model, post‐SVR HCC was positively associated with non‐Caucasian race ( p = 0.03), smoking ( p = 0.03), age > 60 years at HCC diagnosis ( p = 0.03), albumin<3.5 g/dL ( p = 0.02), AST/ALT>1 ( p = 0.05), and platelets <100 × 10 3 cells/μL ( p < 0.001). Alpha fetoprotein ≥4.75 ng/mL had 90% specificity and 71% sensitivity for HCC occurrence. Noncirrhotic patients had larger tumors ( p = 0.002) and a higher prevalence of vascular invasion ( p = 0.016) than cirrhotic patients. Conclusions One‐third of patients with post‐SVR HCC did not have liver cirrhosis; most had no steatosis/steatohepatitis. Hepatocellular carcinomas were more advanced in noncirrhotic patients. Results support AFP as a promising marker of post‐SVR HCC risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".