Surgical Pathology Diagnostic Pitfalls of Hepatoblastoma
Bibliographic record
Abstract
Hepatoblastoma (HB) is the most common malignancy within the rare cohort of pediatric primary liver tumors. It may arise sporadically or in association with germline mutations in specific genetic syndromes. Histogenesis recapitulates fetal hepatic development, however, this tumor can exhibit a markedly heterogeneous appearance both macroscopically and under the microscope. Histologic subtypes are classified based on morphologic appearance, with additional discrimination based on emerging molecular and immunohistochemical features. Numerous diagnostic pitfalls exist from clinical presentation through to ancillary testing; at all stages, the surgical pathologist must be discerning and open to collaboration with colleagues of different specialties. Problematic areas include the adequacy of tissue sampling, correlation of histology with radiologic appearance and alpha feto-protein (AFP) serology, forming a diagnostic consensus within the pediatric pathology community and choosing a shrewd immunohistochemical panel. This review discusses the sequence of events leading up to histologic assessment, and the nuances of microscopic evaluation. Along the way, pitfalls are highlighted, providing a tool for the surgical pathologists to support their individual approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".