Cholangioscopic biopsy sample detection of bile duct invasion by hepatocellular carcinoma: an underappreciated entity
Bibliographic record
Abstract
Background and Aims: Bile duct invasion (BDI) by hepatocellular carcinoma (HCC) is rare and poorly characterized. Our aim was to elucidate clinical, cholangioscopic, and pathologic features of HCC with BDI and to compare them with features of cholangiocarcinoma (CC). Methods: Seven cases of HCC with BDI (6 HCC and 1 combined HCC-CC) and 7 cases of CC diagnosed by cholangioscopic biopsy sampling between 2016 and 2020 were compared. Results: The median age of HCC patients was 64 years (range, 49-77), and 6 patients were men. The median age of CC patients was 73 years (range, 58-75), and 4 patients were men. Obstructive jaundice was the presenting sign in 86% of HCC and 100% of CC cases. Cirrhosis was present in 77% of HCC cases but only 28% of CC cases. α-Fetoprotein was elevated in 57% of HCC cases and none of the CC cases. Both groups had biliary strictures; however, cholangioscopic features of HCC were more likely to show noncircumferential strictures with a mass and were less likely to include ulceration. Villiform formation and frond-like projections were more common in CC. Both showed increased vascularity and friability. Imaging showed a mass in 100% of HCC and in 57% of CC cases. The histopathology of HCC with BDI included trabecular or pseudoglandular architecture, granular eosinophilic cytoplasm, absence of mucin, and atypical nuclear features. Immunohistochemical staining in all HCC cases confirmed a hepatocyte phenotype. Immunohistochemical markers were required to distinguish cases of BDI caused by poorly differentiated HCC or CC because of overlapping clinicopathologic features between the 2 groups. Conclusions: Cholangioscopic findings of a noncircumferential stricture with a luminal mass are indicative of HCC with BDI. Pathologists should routinely use a panel of hepatocyte and cholangiocyte biomarkers to differentiate BDI by HCC from CC and metastases in poorly differentiated carcinoma that lack mucin.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".