Hepatocellular Carcinoma, Fibrolamellar Variant: Diagnostic Pathologic Criteria and Molecular Pathology Update. A Primer
Bibliographic record
Abstract
Fibrolamellar hepatocellular carcinoma (FL-HCC) is generally a fairly rare event in routine pathology practice. This variant of hepatocellular carcinoma (HCC) is peculiarly intriguing and,in addition, poorly understood. Young people or children are often the target individuals with this type of cancer. Previously, I highlighted some pathology aspects of FL-HCC, but in this review, the distinctive clinico-pathologic features of FL-HCC and the diagnostic pathologic criteria of FL-HCC are fractionally reviewed and expanded upon. Further, molecular genetics update data with reference to this specific tumor are particularly highlighted as a primer for general pathologists and pediatric histopathologists. FL-HCC may present with metastases, and regional lymph nodes may be sites of metastatic spread. However, peritoneal and pulmonary metastatic foci have also been reported. To the best of our knowledge, FL-HCC was initially considered having an indolent course, but survival outcomes have recently been updated reconsidering the prognosis of this tumor. Patients seem to respond well to surgical resection, but recurrences are common. Thus, alternative therapies, such as chemotherapy and radiation, are ongoing. Overall, it seems that this aspect has not been well-studied for this variant of HCC and should be considered as target for future clinical trials. Remarkably, FL-HCC data seem to point to a liver neoplasm of uncertain origin and unveiled outcome. A functional chimeric transcript incorporating DNAJB1 and PRKACA was recently added to FL-HCC. This sensational result may give remarkable insights into the understanding of this rare disease and potentially provide the basis for its specific diagnostic marker. Detection of DNAJB1-PRKACA seems to be, indeed, a very sensitive and specific finding in supporting the diagnosis of FL-HCC. In a quite diffuse opinion, prognosis of this tumor should be reconsidered following the potentially mandatory application of new molecular biological tools.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".